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1
Rokamwar, Kaustubh. "Feed- Forward Neural Network based Day Ahead Nodal Pricing." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1029–33. http://dx.doi.org/10.22214/ijraset.2021.36352.
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An electricity locational marginal pricing prediction normally recognized by 24-hour day-ahead nodal price forecast. In this paper first collected all physical and technical data i.e. availability of generation and their cost characteristics, real and reactive demands at various buses, transmission capacity availability at various conditions like peak and off-peak conditions. All these input data are used as input for computation of optimal power flow. The nodal prices are calculated with AC-DC optimal power flow methodology for IEEE 30 bus system. The resulted optimal real electricity bus voltages, nodal prices, reactive and real demands, angles have been given as inputs to Artificial Neural Network (ANN) for predict day ahead nodal prices.
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Marwan, Marwan, and Pirman Pirman. "Mitigating Electricity a Price Spike under Pre-Cooling Method." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (June 1, 2016): 1281. http://dx.doi.org/10.11591/ijece.v6i3.9597.
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The growing demand for air-conditioning is one of the largest contributors to Australia overall electricity consumption. This has started to create peak load supply problems for some electricity utilities particularly in Queensland. This research aimed to develop a consumer demand side response model to assist electricity consumers to mitigate peak demand on the electrical network. The proposed model allows consumers to independently and proactively manage air conditioning peak electricity demand. The main contribution of this research is how to show consumers can mitigate peak demands by optimizing energy costs for air conditioning in a several cases such as no spike and spike considering to the probability spike cases may only occur in the middle of the day for half hour, one hour and one and half hour spikes. This model also investigates how air conditioning applied a pre-cooling method when there is a substantial risk of a price spike. The results indicate the potential of the scheme to achieve energy savings and reducing electricity bills (costs) to the consumer. The model was tested with the Queensland electricity market data from Australian Energy Market Operator and Brisbane temperature data from Bureau statistic during hot days.
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Marwan, Marwan, and Pirman Pirman. "Mitigating Electricity a Price Spike under Pre-Cooling Method." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (June 1, 2016): 1281. http://dx.doi.org/10.11591/ijece.v6i3.pp1281-1293.
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The growing demand for air-conditioning is one of the largest contributors to Australia overall electricity consumption. This has started to create peak load supply problems for some electricity utilities particularly in Queensland. This research aimed to develop a consumer demand side response model to assist electricity consumers to mitigate peak demand on the electrical network. The proposed model allows consumers to independently and proactively manage air conditioning peak electricity demand. The main contribution of this research is how to show consumers can mitigate peak demands by optimizing energy costs for air conditioning in a several cases such as no spike and spike considering to the probability spike cases may only occur in the middle of the day for half hour, one hour and one and half hour spikes. This model also investigates how air conditioning applied a pre-cooling method when there is a substantial risk of a price spike. The results indicate the potential of the scheme to achieve energy savings and reducing electricity bills (costs) to the consumer. The model was tested with the Queensland electricity market data from Australian Energy Market Operator and Brisbane temperature data from Bureau statistic during hot days.
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Kim, Hyunsoo, Jiseok Jeong, and Changwan Kim. "Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network." Mathematics 10, no. 23 (November 28, 2022): 4486. http://dx.doi.org/10.3390/math10234486.
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Forecasting the electricity demand of buildings is a key step in preventing a high concentration of electricity demand and optimizing the operation of national power systems. Recently, the overall performance of electricity-demand forecasting has been improved through the application of long short-term memory (LSTM) networks, which are well-suited to processing time-series data. However, previous studies have focused on improving the accuracy in forecasting only overall electricity demand, but not peak demand. Therefore, this study proposes adding residual learning to the LSTM approach to improve the forecast accuracy of both peak and total electricity demand. Using a residual block, the residual LSTM proposed in this study can map the residual function, which is the difference between the hypothesis and the observed value, and subsequently learn a pattern for the residual load. The proposed model delivered root mean square errors (RMSE) of 10.5 and 6.91 for the peak and next-day electricity demand forecasts, respectively, outperforming the benchmark models evaluated. In conclusion, the proposed model provides highly accurate forecasting information, which can help consumers achieve an even distribution of load concentration and countries achieve the stable operation of the national power system.
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Ifaei, P., J. K. Park, T. Y. Woo, C. H. Jeong, and C. K. Yoo. "Leveraging media for demand control in an optimal network of renewable microgrids with hydrogen facilities in South Korea." IOP Conference Series: Earth and Environmental Science 1372, no. 1 (July 1, 2024): 012005. http://dx.doi.org/10.1088/1755-1315/1372/1/012005.
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Abstract In pursuit of a sustainable 2030 strategy in the Republic of Korea, this study addresses the oversight in recent optimal renewable energy microgrid designs, which, despite encompassing all feasible renewable sources, neglected the pivotal role of hydrogen as an energy carrier. This research explores the feasibility of reprogramming media platforms to dynamically shape energy consumption during peak intervals. It further proposes the retrofitting of microgrids with industrial hydrogen production and storage facilities, aligning with controlled electricity demand. A comprehensive social survey investigates the impact of media content on energy-conscious behaviour and cooperation, specifically targeting energy savings during peak hours. Utilizing a probabilistic model, the study quantifies responses from the surveyed sample and decomposes the energy demand time series to reveal three new consumption patterns: demand reduction by lowering residential electricity consumption at peak intervals without shifts, intense demand shifting by redistributing electricity consumption from peaks to valleys without human intervention, and moderate demand shifting achieved through cooperation with consumers. With these novel energy demand patterns in hand, the study optimally designs renewable microgrids in 17 sites in South Korea, comparing two strategies: Plan A, involving electrolysis-based hydrogen production and storage tanks, and Plan B, which excludes hydrogen facilities. Comparative results demonstrate that media content contributes to a 10.28% and 16.11% reduction in peak electricity consumption, with and without human intervention, respectively. In Plan B, a demand cut saves 937.3 MWh/yr, resulting in a 12.88% reduction in the levelized costs of electricity (LCOE) and a 4.67% reduction in net present costs (NPC) of optimal renewable microgrids in Korea. Conversely, in Plan A, intense demand reduction exhibits superior performance, leading to $981K less NPC, 1,046 MWh/yr less excess electricity, and a 3.76% smaller LCOE. The study recommends the implementation of smart gadgets to control residential electricity consumption, producing industrial hydrogen at Korean sites based on consumer attention and agreement with specific media content. However, it underscores the importance of studying the socio-psychological effects of this plan in future research.
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Gupta, Rajat, and Sahar Zahiri. "Examining daily electricity demand and indoor temperature profiles in UK social housing flats retrofitted with heat pumps." IOP Conference Series: Earth and Environmental Science 1363, no. 1 (June 1, 2024): 012093. http://dx.doi.org/10.1088/1755-1315/1363/1/012093.
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Abstract The UK Government has announced decarbonisation of domestic heating thorough low carbon heat pumps. This will result in the deployment of 5 million heat pumps by 2030, and over 25 million by 2050, thereby increasing seasonal and daily peak electricity demand and putting a strain on local electricity networks. Despite this, there is limited evidence on the impact of heat pump operation on daily electricity demand profiles. This paper empirically examines the impact of retrofitted ground source heat pump (GSHP) on daily electricity demand of six social housing flats co-located in a socially deprived housing estate in Oxford (UK) to understand the changes on daily electricity demand during the evening peak period (4-7pm). Concurrent time-series monitoring of electricity use, and indoor-outdoor temperatures was undertaken for one week before and after heat pumps installation during the heating season of 2020-2021. Contextual data about the flats was gathered using householder surveys and Energy Performance Certificates (EPCs). Pre-heat pump, electricity demand peaked during night time from 12am to 2am due to the use of night storage heaters. Post heat pump installation, mean indoor temperature and electricity demand profiles became more stable. While mean daily electricity use reduced by 42%, peak daily electricity use increased by 23%. Despite a small sample, the magnitude and timing of the peak period of electricity use varied. This reinforces the need for enabling flexible heat pump operation through time-of-use tariffs to bring value for local electricity network and householders.
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Nafkha, Rafik, Tomasz Ząbkowski, and Krzysztof Gajowniczek. "Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers." Energies 14, no. 8 (April 14, 2021): 2181. http://dx.doi.org/10.3390/en14082181.
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The electricity tariffs available to customers in Poland depend on the connection voltage level and contracted capacity, which reflect the customer demand profile. Therefore, before connecting to the power grid, each consumer declares the demand for maximum power. This amount, referred to as the contracted capacity, is used by the electricity provider to assign the proper connection type to the power grid, including the size of the security breaker. Maximum power is also the basis for calculating fixed charges for electricity consumption, which is controlled and metered through peak meters. If the peak demand exceeds the contracted capacity, a penalty charge is applied to the exceeded amount, which is up to ten times the basic rate. In this article, we present several solutions for entrepreneurs based on the implementation of two-stage and deep learning approaches to predict maximal load values and the moments of exceeding the contracted capacity in the short term, i.e., up to one month ahead. The forecast is further used to optimize the capacity volume to be contracted in the following month to minimize network charge for exceeding the contracted level. As confirmed experimentally with two datasets, the application of a multiple output forecast artificial neural network model and a genetic algorithm (two-stage approach) for load optimization delivers significant benefits to customers. As an alternative, the same benefit is delivered with a deep learning architecture (hybrid approach) to predict the maximal capacity demands and, simultaneously, to determine the optimal capacity contract.
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Dejvises, Jackravut. "Energy Storage System Sizing for Peak Shaving in Thailand." ECTI Transactions on Electrical Engineering, Electronics, and Communications 14, no. 1 (November 30, 2015): 49–55. http://dx.doi.org/10.37936/ecti-eec.2016141.171094.
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This paper presents a mathematical model of energy storage systems (ESSs) to minimise daily electrical peak power demand in Thailand. A daily electrical load curve on a peak day obtained from Electricity Generating Authority of Thailand (EGAT) is used to analyse the capability of energy storage system for electrical peak power demand reduction with different ESS sizes. It is found that with power rate of 50 percent of the difference between the minimum and the maximum demands of the daily load curve and with energy capacity of 50 percent of the sum of each time step absolute energy difference between the demand and the average demand of the daily load curve, ESS can decrease daily electrical peak demand approximately 7.4 percent and increase daily load factor approximately 9.9 percent.
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Kauko, Hanne, Daniel Rohde, and Armin Hafner. "Local Heating Networks with Waste Heat Utilization: Low or Medium Temperature Supply?" Energies 13, no. 4 (February 20, 2020): 954. http://dx.doi.org/10.3390/en13040954.
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District heating enables an economical use of energy sources that would otherwise be wasted to cover the heating demands of buildings in urban areas. For efficient utilization of local waste heat and renewable heat sources, low distribution temperatures are of crucial importance. This study evaluates a local heating network being planned for a new building area in Trondheim, Norway, with waste heat available from a nearby ice skating rink. Two alternative supply temperature levels have been evaluated with dynamic simulations: low temperature (40 °C), with direct utilization of waste heat and decentralized domestic hot water (DHW) production using heat pumps; and medium temperature (70 °C), applying a centralized heat pump to lift the temperature of the waste heat. The local network will be connected to the primary district heating network to cover the remaining heat demand. The simulation results show that with a medium temperature supply, the peak power demand is up to three times higher than with a low temperature supply. This results from the fact that the centralized heat pump lifts the temperature for the entire network, including space and DHW heating demands. With a low temperature supply, heat pumps are applied only for DHW production, which enables a low and even electricity demand. On the other hand, with a low temperature supply, the district heating demand is high in the wintertime, in particular if the waste heat temperature is low. The choice of a suitable supply temperature level for a local heating network is hence strongly dependent on the temperature of the available waste heat, but also on the costs and emissions related to the production of district heating and electricity in the different seasons.
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Amin, Adil, Wajahat Ullah Khan Tareen, Muhammad Usman, Haider Ali, Inam Bari, Ben Horan, Saad Mekhilef, Muhammad Asif, Saeed Ahmed, and Anzar Mahmood. "A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network." Sustainability 12, no. 23 (December 4, 2020): 10160. http://dx.doi.org/10.3390/su122310160.
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This study summarizes a critical review on EVs’ optimal charging and scheduling under dynamic pricing schemes. A detailed comparison of these schemes, namely, Real Time Pricing (RTP), Time of Use (ToU), Critical Peak Pricing (CPP), and Peak Time Rebates (PTR), is presented. Globally, the intention is to reduce the carbon emissions (CO2) has motivated the extensive practice of Electric Vehicles (EVs). The uncoordinated charging and uncontrolled integration however of EVs to the distribution network deteriorates the system performance in terms of power quality issues. Therefore, the EVs’ charging activity can be coordinated by dynamic electricity pricing, which can influence the charging activities of the EVs customers by offering flexible pricing at different demands. Recently, with developments in technology and control schemes, the RTP scheme offers more promise compared to the other types of tariff because of the greater flexibility for EVs’ customers to adjust their demands. It however involves higher degree of billing instability, which may influence the customer’s confidence. In addition, the RTP scheme needs a robust intelligent automation system to improve the customer’s feedback to time varying prices. In addition, the review covers the main optimization methods employed in a dynamic pricing environment to achieve objectives such as power loss and electricity cost minimization, peak load reduction, voltage regulation, distribution infrastructure overloading minimization, etc.
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Gutiérrez-Villegas, Juan Carlos, Set Vejar Ruíz, and Agustín Escamilla Martínez. "PV system interconnected to the electricity grid with hourly control of energy injection." DYNA 88, no. 217 (May 10, 2021): 84–90. http://dx.doi.org/10.15446/dyna.v88n217.88789.
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Implementing Photovoltaic Systems (SFV) Interconnected to the electrical network, the energy consumption of the network is reduced, thus the SVF is a good alternative in rates where the charge is solely for energy consumption. In this work is presented the analysis of the Great Demand rate in Medium Hourly Voltage (GDMTH) that will allow designing SFV interconnected to the network helping to reduce the maximum demand for peak hours and increase the impact of the generation of energy through the PV system at rates where hourly consumption is considered. The consumption of energy by the user is analyzed, identifying that the highest demand for energy occurs in the intermediate hours and lower consumption in peak hours, however reviewing the electricity bill, the charge for consumption during peak hours increases significantly, SFV sizing is presented to reduce the estimated consumption during peak hours and the return on investment is determined. Subsequently, the implementation of a prototype to generate, store, and manage the injection of energy during peak hours is presented, analyzing the impact on the electricity bill, therefore the reduction of cost demand and energy in this hour time, is corroborated with a reduction up to 37% of monthly electricity bill.
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Cahyo, Mukti Dwi, Sri Heranurweni, and Harmini Harmini. "PREDIKSI BEBAN ENERGI LISTRIK APJ KOTA SEMARANG MENGGUNAKAN METODE RADIAL BASIS FUNCTION (RBF)." Elektrika 11, no. 2 (October 8, 2019): 21. http://dx.doi.org/10.26623/elektrika.v11i2.1699.
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Electric power is one of the main needs of society today, ranging from household consumers to industry. The demand for electricity increases every year. So as to achieve adjustments between power generation and power demand, the electricity provider (PLN) must know the load needs or electricity demand for some time to come. There are many studies on the prediction of electricity loads in electricity, but they are not specific to each consumer sector. One of the predictions of this electrical load can be done using the Radial Basis Function Artificial Neural Network (ANN) method. This method uses training data learning from 2010 - 2017 as a reference data. Calculations with this method are based on empirical experience of electricity provider planning which is relatively difficult to do, especially in terms of corrections that need to be made to changes in load. This study specifically predicts the electricity load in the Semarang Rayon network service area in 2019-2024. The results of this Artificial Neural Network produce projected electricity demand needs in 2019-2024 with an average annual increase of 1.01% and peak load in 2019-2024. The highest peak load in 2024 and the dominating average is the household sector with an increase of 1% per year. The accuracy results of the Radial Basis Function model reached 95%.
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Sattar, Mahroo, Mahmoud Samiei Moghaddam, Azita Azarfar, Nasrin Salehi, and Mojtaba Vahedi. "Co-optimization of integrated energy systems in the presence of renewable energy, electric vehicles, power-to-gas systems and energy storage systems with demand-side management." Clean Energy 7, no. 2 (March 31, 2023): 426–35. http://dx.doi.org/10.1093/ce/zkad011.
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Abstract With the widespread penetration of renewable energy sources and energy storage systems, the problem of energy management has received increasing attention. One of the systems that network owners consider today is the power-to-gas (P2G) system. This system causes surplus electricity generated from renewable energy resources or batteries in the network to be converted into gas and sold to the gas network. Two reasons for the existence of gas distributed generation resources and P2G systems cause the two power and gas networks to interact. Energy management and profit making considering these two networks, as a co-optimization of integrated energy systems, is a topic that has been discussed in this study to achieve the best optimal answer. Since the production of renewable energy resources and the purchase price of energy are uncertain, a scenario-based method has been chosen for modelling. Demand-side management is also one of the important problems in optimal operation of the electricity network, which can have a significant impact on reducing peak load and increasing profits. In this paper, a mixed-integer quadratic programming model for co-optimization of electric distribution and gas networks in the presence of distributed generation resources, P2G systems, storage facilities, electric vehicles and demand-side management is presented. The 33-bus distribution network is intended to analyse the proposed model. The results of different scenarios show the efficiency of the proposed model. Several key points are deduced from the obtained results: (i) demand-side management is able to reduce the peak load of the network, (ii) the presence of renewable resources and batteries can cause the network to convert excess electricity into gas and sell it to the gas network in the market and (iii) distributed generation can reduce the purchase of energy from the upstream network and cause a 36% reduction in the cost function.
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Zhao, Mengchen, Santiago Gomez-Rosero, Hooman Nouraei, Craig Zych, Miriam A. M. Capretz, and Ayan Sadhu. "Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning." Energies 17, no. 7 (April 1, 2024): 1672. http://dx.doi.org/10.3390/en17071672.
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Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption.
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Mohammad, Mirzaei Talabari. "Optimization of grid-connected MicroGrid demand considering demand response." National Security and Strategic Planning 2024, no. 1 (September 23, 2024): 60–65. http://dx.doi.org/10.37468/2307-1400-2024-1-60-65.
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Electricity grid is a product of urbanization expansion and rapid development of various infrastructures worldwide and over the past centuries. Although power companies are located in diverse regions, they typically use the same technologies to generate and distribute electricity. Proper implementation of the demand response (DR) program should be provided with some equipment to make subscribers aware of electricity price at any time and accordingly provide a proper response to the grid to reduce costs. This, in turn, reduces demand during peak hours. The intelligent grid, using the two-way communication network and the transmission of information to subscribers, and an advanced metering network provide a good structure for fully implementing DR programs. The present study applies a demand response economic model to implement DRs. The model uses price elasticity of demand, which provides subscribers a more precise consumption behavior regarding the factors influencing demand (e.g., electricity prices, bonuses, and fines). Applying the model to the microgrid, the operating cost significantly decreases in both operating modes.
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Sara Alimammadova, Sara Alimammadova. "ANALYSIS REDUCTION OF ENERGY LOSSES IN DISTRIBUTION NETWORKS." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 38, no. 03 (March 28, 2024): 297–305. http://dx.doi.org/10.36962/pahtei38032024-297.
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The article analyzes the reduction of energy losses in distribution networks, which is a serious problem requiring an integrated approach, including technological, regulatory, and behavioral interventions. Some measures that can be developed and implemented to solve this problem are given, such as infrastructure modernization, taking into account investments in the modernization of distribution infrastructure, affecting a significant reduction in energy loss, and the introduction of load management strategies that optimize energy distribution and reduce losses. This includes measures such as load balancing, voltage regulation, and demand management programs to shift peak loads to off-peak hours. The integration of smart grid technologies allows monitoring and management of the distribution network in real-time. This includes the deployment of advanced measurement infrastructure, sensors, and automation systems to more effectively detect and reduce energy losses. Technological losses are associated with the technology of the process of transmission of electricity through networks. Commercial losses are measured by the difference between actual estimated losses and technological losses, taking into account commercial losses, electric energy entering the electric grid, electric energy supplied to consumers, and electricity costs for own needs of substations. Circuit engineering methods for accounting for energy losses in electrical networks are used in various combinations. At the same time, it should be borne in mind that the sequence of reporting operations using the results of specified specific parameters should give an accurate result. To determine the loss of electricity, data on the minimum and maximum load of the network and the amount of electricity consumed over the same period are required. The calculation is given by the method of average load. Information about the minimum and maximum network load is a key element for effective design, management and maintenance of electrical networks. This information allows you to optimize the use of resources and ensure reliable network operation in various conditions. Minimum network load - the minimum load is the minimum amount of energy that is consumed by the network during a certain period of time, for example, during night hours or periods of low activity. Information about the minimum load allows you to optimize network operation, for example, by turning off part of the etquipment or managing energy consumption during off-peak periods. Maximum network load - is the maximum amount of energy consumed by the network at a given time. This may be during periods of peak demand, for example, during a heat wave when a lot of air conditioners are running, or during an increase in production in industrial areas. The maximum load information allows you to determine the required power and capacity of the network equipment to ensure reliable operation under maximum load conditions. Keywords: distribution networks, network load, average load, power, period, power factor, electrical energy, duty cycle, consumers, practical research, voltage.
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Byk, Felix, Yuri Kakosha, and Lyudmila Myshkina. "Distributed power generation and power supply reliability improvement." E3S Web of Conferences 216 (2020): 01013. http://dx.doi.org/10.1051/e3sconf/202021601013.
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The appearance of distributed generation in the power supply systems of industrial enterprises leads to the emergence of requirements for networks to increase their redundancy functions. The introduction of network redundancy fees will lead to an increase in electricity supply costs for such enterprises. The source of additional revenue may be the provision of regulatory resources to the aggregator of electricity demand management in the UES of Russia. But this requires changes that allow active consumers to supply the distribution network with excess capacity during peak hours in the UES of Russia. The article shows the efficiency of operation in the mode without power supply to the distribution network. This mode does not lead to a decrease in revenues from network services for the transmission of electricity. The proposed changes will lead to an increase in the reliability of power supply and increase the economic efficiency of the UES of Russia.
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Rouhani, Anise, Habib Rajabi Mashhadi, and Mehdi Feizi. "Estimating the Short-term Price Elasticity of Residential Electricity Demand in Iran." International Transactions on Electrical Energy Systems 2022 (August 13, 2022): 1–8. http://dx.doi.org/10.1155/2022/4233407.
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Excessive electricity consumption causes severe problems in the electricity sector and consequently in load curtailment. This paper estimates the short-term price elasticity of electricity demand for the Iranian household sector by monthly panel dataset. The estimated short-term price elasticity of electricity demand was −0.048. We use abrupt change in electricity price due to targeting subsidy on December 18th, 2010. The results show significant heterogeneity in electricity price elasticity between the various levels of consumption. Due to the heterogeneity of consumers’ electricity price elasticity, we can categorize residential consumers into four groups. Hence, policymakers are suggested to manage peak loads in the electricity network by estimating consumer responsiveness and reforming electricity pricing considering equality issues and tariff design.
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Tan, Yetuo, Yongming Zhi, Zhengbin Luo, Honggang Fan, Jun Wan, and Tao Zhang. "Optimal Scheduling of Virtual Power Plant with Flexibility Margin Considering Demand Response and Uncertainties." Energies 16, no. 15 (August 7, 2023): 5833. http://dx.doi.org/10.3390/en16155833.
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The emission reduction of global greenhouse gases is one of the key steps towards sustainable development. Demand response utilizes the resources of the demand side as an alternative of power supply which is very important for the power network balance, and the virtual power plant (VPP) could overcome barriers to participate in the electricity market. In this paper, the optimal scheduling of a VPP with a flexibility margin considering demand response and uncertainties is proposed. Compared with a conventional power plant, the cost models of VPPs considering the impact of uncertainty and the operation constraints considering demand response and flexibility margin characteristics are constructed. The orderly charging and discharging strategy for electric vehicles considering user demands and interests is introduced in the demand response. The research results show that the method can reduce the charging cost for users participating in reverse power supply using a VPP. The optimizing strategy could prevent overload, complete load transfer, and realize peak shifting and valley filling, solving the problems of the new peak caused by disorderly power utilization.
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Hj Osman, Muhamad Suhaimi, Ho Wai Shin, Arfah Diyanah Nizamuddin, Zarina Ab Muis, Wong Keng Yinn, and Tan Huiyi. "Vehicle To Building (V2b) Peak Load Shaving and Tariff Analysis." IOP Conference Series: Earth and Environmental Science 1395, no. 1 (September 1, 2024): 012019. http://dx.doi.org/10.1088/1755-1315/1395/1/012019.
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Abstract Commercial buildings are essentially important energy consumers for national electricity grid provider as it’s owned by businesses owners that directly keep up the national gross domestic product (GDP). Soaring load demand from commercial buildings usually occurs within few hours on peak business hour. Electricity grid infrastructure designed to support maximum demand of the system but underutilize most of the time outside the peak session. Growing number of EV penetration in local market can serve as mobile energy storage for Vehicle to Building (V2B) energy integration thus enable peak load shaving to minimize maximum demand during peak period. This study presents a feasible methodology approach on determining the suitable V2B tariff on several peak load shaving scenarios to provide attractive return to building owner and discounted off-peak tariff to accommodate lower EV owner’s charging cost. By having the combination of attractive V2B and off-peak tariff that benefits both sides, subsequently increase EV penetration for V2B. It may also charm EV markets as it minimizes the total cost of EV ownership and reduce maximum demand from electricity grid. Hence it reduces the transportation CO2 emissions and contribution toward optimization of grid network electrical infrastructure of transmission and distribution systems design with lower maximum design system requirement.
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Ivanov, Ovidiu, Samiran Chattopadhyay, Soumya Banerjee, Bogdan-Constantin Neagu, Gheorghe Grigoras, and Mihai Gavrilas. "A Novel Algorithm with Multiple Consumer Demand Response Priorities in Residential Unbalanced LV Electricity Distribution Networks." Mathematics 8, no. 8 (July 24, 2020): 1220. http://dx.doi.org/10.3390/math8081220.
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Demand Side Management (DSM) is becoming necessary in residential electricity distribution networks where local electricity trading is implemented. Amongst the DSM tools, Demand Response (DR) is used to engage the consumers in the market by voluntary disconnection of high consumption receptors at peak demand hours. As a part of the transition to Smart Grids, there is a high interest in DR applications for residential consumers connected in intelligent grids which allow remote controlling of receptors by electricity distribution system operators and Home Energy Management Systems (HEMS) at consumer homes. This paper proposes a novel algorithm for multi-objective DR optimization in low voltage distribution networks with unbalanced loads, that takes into account individual consumer comfort settings and several technical objectives for the network operator. Phase load balancing, two approaches for minimum comfort disturbance of consumers and two alternatives for network loss reduction are proposed as objectives for DR. An original and faster method of replacing load flow calculations in the evaluation of the feasible solutions is proposed. A case study demonstrates the capabilities of the algorithm.
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Ge, Xiaoxue, Zhijie Liu, Kejun Li, Chenxian Guo, Gang Shen, and Zichen Wang. "Economic Scheduling Strategy for Multi-Energy-Integrated Highway Service Centers Considering Carbon Trading and Critical Peak Pricing Mechanism." Symmetry 16, no. 9 (August 26, 2024): 1110. http://dx.doi.org/10.3390/sym16091110.
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This study proposes an optimized economic scheduling strategy for multi-energy-integrated highway service centers (MEIHSCs) within a 24 h operational timeframe. With the imperative of carbon peaking and carbon neutrality, highway areas are increasingly incorporating renewable energy systems, such as photovoltaic arrays, to capitalize on abundant resources along highways. Considering the diverse load demands of new energy vehicles and the mismatch between energy supply and demand on the highway, MEIHSCs must adapt to these trends by establishing integrated networks for electricity, natural gas, and hydrogen refueling. However, there is a lack of coordination between equipment switching and the phases of low electricity prices and peak renewable energy periods. To address this challenge and improve economic efficiency, this study proposes an economic dispatch strategy that combines economic incentives based on carbon trading and critical peak pricing mechanisms. This strategy aims to maximize economic benefits while fully meeting the load demands of new energy vehicles. Case studies indicate that operating costs are reduced by 28.04% compared to strategies without new energy installations, and by 47.85% compared to strategies without optimization. The results demonstrate that this integrated and optimized strategy significantly reduces energy costs and enhances economic benefits in highway service centers.
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Borghini, Eugenio, Cinzia Giannetti, James Flynn, and Grazia Todeschini. "Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation." Energies 14, no. 12 (June 10, 2021): 3453. http://dx.doi.org/10.3390/en14123453.
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The growing adoption of decentralised renewable energy generation (such as solar photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain experienced by the distribution networks in the near future. In such a scenario, energy storage is becoming a key alternative to traditional expensive reinforcements to network infrastructure, due to its flexibility, decreasing costs and fast deployment capabilities. In this work, an end-to-end data-driven solution to optimally design the control of a battery unit with the aim of reducing the peak electricity demand is presented. The proposed solution uses state-of-the-art machine learning methods for forecasting electricity demand and PV generation, combined with an optimisation strategy to maximise the use of photovoltaic energy to charge the energy storage unit. To this end, historical demand, weather, and solar energy generation data collected at the Stentaway Primary substation near Plymouth, UK, and at other six locations were employed.
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Senchilo, Nikita Dmitrievich, and Denis Anatolievich Ustinov. "Method for Determining the Optimal Capacity of Energy Storage Systems with a Long-Term Forecast of Power Consumption." Energies 14, no. 21 (October 30, 2021): 7098. http://dx.doi.org/10.3390/en14217098.
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The unevenness of the electricity consumption schedule at enterprises leads to a peak power increase, which leads to an increase in the cost of electricity supply. Energy storage devices can optimize the energy schedule by compensating the planned schedule deviations, as well as reducing consumption from the external network when participating in a demand response. However, during the day, there may be several peaks in consumption, which lead to a complete discharge of the battery to one of the peaks; as a result, total peak power consumption does not decrease. To optimize the operation of storage devices, a day-ahead forecast is often used, which allows to determine the total number of peaks. However, the power of the storage system may not be sufficient for optimal peak compensation. In this study, a long-term forecast of power consumption based on the use of exogenous parameters in the decision tree model is used. Based on the forecast, a novel algorithm for determining the optimal storage capacity for a specific consumer is developed, which optimizes the costs of leveling the load schedule.
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El-Hafez, Omar Jouma, Tarek Y. ElMekkawy, Mohamed Kharbeche, and Ahmed Massoud. "Impact of COVID-19 Pandemic on Qatar Electricity Demand and Load Forecasting: Preparedness of Distribution Networks for Emerging Situations." Sustainability 14, no. 15 (July 29, 2022): 9316. http://dx.doi.org/10.3390/su14159316.
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The COVID-19 pandemic has brought several global challenges, one of which is meeting the electricity demand. Millions of people are confined to their homes, in each of which a reliable electricity supply is needed, to support teleworking, e-commerce, and electrical appliances such as HVAC, lighting, fridges, water heaters, etc. Furthermore, electricity is also required to operate medical equipment in hospitals and perhaps temporary quarantine hospitals/shelters. Electricity demand forecasting is a crucial input into decision-making for electricity providers. Without an accurate forecast of electricity demand, over-capacity or shortages in the power supply may result in high costs, network bottlenecks, and instability. Electricity demand can be divided, typically, into two sectors: domestic and industrial. This paper discusses the impact of the COVID 19 pandemic on Qatar’s electricity demand and forecasting. It is noted that students’ and employees’ attendance are the restrictions with the highest impact on electricity demand. There was an increase of nearly 28% in the domestic peak due to the attendance of 30% of school students. Furthermore, in this study, historical data on Qatar’s electricity demand, population, and GDP were collected, along with information on COVID-19 restrictions. Statistical analysis was used to unfold the impact of the COVID-19 pandemic. The results and findings will help decision-makers and planners manage future electricity demand, and support distribution networks’ preparedness for emerging situations.
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Ramsebner, Jasmine, Albert Hiesl, and Reinhard Haas. "Efficient Load Management for BEV Charging Infrastructure in Multi-Apartment Buildings." Energies 13, no. 22 (November 13, 2020): 5927. http://dx.doi.org/10.3390/en13225927.
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Interest in and demand for battery electric vehicles (BEVs) is growing strongly due to the increasing awareness of climate change and specific decarbonization goals. One of the largest challenges remains the provision of large-scale, efficient charging infrastructure in multi-apartment buildings. Successful load management (LM) for BEV charging directly influences the technical requirements and the economic and environmental aspects of charging infrastructure and can prevent costly distribution grid expansion. The main objective of this paper is to evaluate potential LM approaches in multi-apartment buildings to avoid an increase in existing electricity demand peaks with BEV diffusion. Using our model parameters, off-peak charging achieved a 40% reduction in the building’s demand peak at 100% BEV diffusion compared to uncontrolled charging and reduced the correlation between BEV charging and the national share of thermal power generation. The most efficient charging capacity in the private network was achieved at 0.44 kW/BEV. A verification of the model results with the demonstration phase of the “Urcharge” project supports our overall findings. Our results outline the advantages of LM across a large-scale BEV charging network to control the impact on the electricity system along with the diffusion of e-mobility.
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Bunnoon, Pituk. "Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (February 1, 2016): 12. http://dx.doi.org/10.11591/ijece.v6i1.8901.
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One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation.
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Bunnoon, Pituk. "Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (February 1, 2016): 12. http://dx.doi.org/10.11591/ijece.v6i1.pp12-20.
Full textAbstract:
One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation.
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Karamanski, Stefan, and Gareth Erfort. "Wind Energy Supply Profiling and Offshore Potential in South Africa." Energies 16, no. 9 (April 24, 2023): 3668. http://dx.doi.org/10.3390/en16093668.
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South Africa’s energy network is under severe pressure due to low supply and overwhelming demand. With an increase in renewable energy providers, specifically wind energy, knowing how the supply can satisfy the electricity demand may relieve apprehensions. This research aims to provide insight into the wind energy supply of South Africa and question how well this supply meets the demand of South Africa. The methodology used in this work highlights the importance of access to public datasets to dispel misconceptions in the energy industry. Additionally, the work supports network planning and the arguments for increasing wind energy penetration on the South African grid. Wind profiles and the typical energy production of South African wind farms are compared to electricity demand. The geographical spacing of the operational wind farms is considered. It is observed that wind energy supply assists in the peak electricity hourly demand as well as seasonal peaks. Furthermore, South Africa’s coast is analysed to determine the offshore wind power potential, where shallow and deep waters are considered. It is observed that South Africa has a high potential for offshore wind, even after losses are applied.
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Li, Fei, Bo Gao, Lun Shi, Hongtao Shen, Peng Tao, Hongxi Wang, Yehua Mao, and Yiyi Zhao. "A Time-Varying Incentive Optimization for Interactive Demand Response Based on Two-Step Clustering." Information 13, no. 9 (September 7, 2022): 421. http://dx.doi.org/10.3390/info13090421.
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With the increasing marketization of electricity, residential users are gradually participating in various businesses of power utility companies, and there are more and more interactive adjustments between load, source, and grid. However, the participation of large-scale users has also brought challenges to the grid companies in carrying out demand-side dispatching work. The user load response is uneven, and users’ behavioral characteristics are highly differentiated. It is necessary to consider the differences in users’ electricity consumption demand in the design of the peak–valley load time-sharing incentives, and to adopt a more flexible incentive form. In this context, this paper first establishes a comprehensive clustering method integrating k-means and self-organizing networks (SONs) for the two-step clustering and a BP neural network for reverse adjustment and correction. Then, a time-varying incentive optimization for interactive demand response based on two-step clustering is introduced. Furthermore, based on the different clustering results of customers, the peak–valley load time-sharing incentives are formulated. The proposed method is validated through case studies, where the results indicate that our method can effectively improve the users’ load characteristics and reduce the users’ electricity costs compared to the existing methods.
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Mele, Enea, Anastasios Natsis, Aphrodite Ktena, Christos Manasis, and Nicholas Assimakis. "Electromobility and Flexibility Management on a Non-Interconnected Island." Energies 14, no. 5 (March 1, 2021): 1337. http://dx.doi.org/10.3390/en14051337.
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The increasing penetration of electrical vehicles (EVs), on the way to decarbonizing the transportation sector, presents several challenges and opportunities for the end users, the distribution grid, and the electricity markets. Uncontrollable EV charging may increase peak demand and impact the grid stability and reliability, especially in the case of non-interconnected microgrids such as the distribution grids of small islands. On the other hand, if EVs are considered as flexible loads and distributed storage, they may offer Vehicle to Grid (V2G) services and contribute to demand-side management through smart charging and discharging. In this work, we present a study on the penetration of EVs and the flexibility they may offer for services to the grid, using a genetic algorithm for optimum valley filling and peak shaving for the case of a non-interconnected island where the electricity demand is several times higher during the summer due to the influx of tourists. Test cases have been developed for various charging/discharging strategies and mobility patterns. Their results are discussed with respect to the current generating capacity of the island as well as the future case where part of the electricity demand will have to be met by renewable energy sources, such as photovoltaic plants, in order to minimize the island’s carbon footprint. Higher EV penetration, in the range of 20–25%, is enabled through smart charging strategies and V2G services, especially for load profiles with a large difference between the peak and low demands. However, the EV penetration and available flexibility is subject to the mobility needs and limited by the population and the size of the road network of the island itself rather than the grid needs and constraints. Limitations and challenges concerning efficient V2G services on a non-interconnected microgrid are identified. The results will be used in the design of a smart charging controller linked to the microgrid’s energy management system.
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Virupaksha, Vinay, Mary Harty, and Kevin McDonnell. "Microgeneration of Electricity Using a Solar Photovoltaic System in Ireland." Energies 12, no. 23 (December 3, 2019): 4600. http://dx.doi.org/10.3390/en12234600.
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Microgeneration of electricity using solar photovoltaic (PV) systems is a sustainable form of renewable energy, however uptake in Ireland remains very low. The aim of this study is to assess the potential of the community-based roof top solar PV microgeneration system to supply electricity to the grid, and to explore a crowd funding mechanism for community ownership of microgeneration projects. A modelled microgeneration project was developed: the electricity load profiles of 68 residential units were estimated; a community-based roof top solar PV system was designed; an electricity network model, based on a real network supplying a town and its surrounding areas, was created; and power flow analysis on the electrical network for system peak and minimum loads was carried out. The embodied energy, energy payback time, GHG payback time, carbon credits and financial cost relating to the proposed solar PV system were calculated. Different crowdfunding models were assessed. Results show the deployment of community solar PV system projects have significant potential to reduce the peak demand, smooth the load profile, assist in the voltage regulation and reduce electrical losses and deliver cost savings to distribution system operator and the consumer.
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Mazibuko, Thokozile, Katleho Moloi, and Kayode Akindeji. "Techno-Economic Design and Optimization of Hybrid Energy Systems." Energies 17, no. 16 (August 22, 2024): 4176. http://dx.doi.org/10.3390/en17164176.
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The power generation capacity must be increased to accommodate population growth and address the lack of electricity access in rural areas. Traditional power plants in South Africa are unable to keep up with the growing demand for electricity. By strategically planning and building clusters of renewable energy sources like solar and wind, microgrid operators can provide a sustainable solution that boosts electricity supply while being cost-effective and environmentally friendly. Utilizing renewable energy can help alleviate strain on power plants by reducing peak demand in constrained distribution networks. The benefits of renewable energy include lower electricity expenses, enhanced system reliability, investment reallocation, and reduced environmental impact. These advantages will enhance the efficiency of the power system and contribute economic value to society. However, integrating solar power into the network infrastructure presents challenges such as fundamental changes in network structure, its intermittent nature due to unpredictability, and geographical constraints, which can complicate the task of grid operators in balancing electricity supply and demand within system limits while minimizing costs. The study employs Homer Pro 3.18.1 software to assess the economic costs and benefits of effectively integrating renewable technologies into the power grid. The aim is to evaluate the economic and technical feasibility of investing in renewable energy projects within the network. The research outcomes can guide power system operators, planners, and designers in leveraging solar energy to drive economic growth and industrial advancement, as well as assist independent power producers in making informed investment choices.
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El-Bayeh, Claude Ziad, Ursula Eicker, Khaled Alzaareer, Brahim Brahmi, and Mohamed Zellagui. "A Novel Data-Energy Management Algorithm for Smart Transformers to Optimize the Total Load Demand in Smart Homes." Energies 13, no. 18 (September 22, 2020): 4984. http://dx.doi.org/10.3390/en13184984.
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The increased integration of Electric Vehicles (EVs) into the distribution network can create severe issues—especially when demand response programs and time-varying electricity prices are applied, EVs tend to charge during the off-peak time to minimize the electricity cost. Hence, another peak demand might be created, and other solutions are required. Many researchers tried to solve the problem; however, limitations exist because of the decentralized topology of the network. The system operator is not allowed to control the end-users’ load due to security and privacy issues. To overcome this situation, we propose a novel data-energy management algorithm on the transformer’s level that controls the power demand profiles of the householders and exchange energy between them without violating their privacy and security. Our method is compared to an existing one in the literature based on a decentralized control strategy. Simulations show that our approach has reduced the electricity cost of the end-users by 3%, increased the revenue of the system operator, and reduced techno-economic losses by 50% and 42%, respectively. Our strategy shows better performance even with a 100% penetration level of EVs on the network, in which it respects the network’s constraints and maintains the voltage within the recommended limits.
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Davari, Mohammad Mehdi, Hossein Ameli, Mohammad Taghi Ameli, and Goran Strbac. "Impact of Local Emergency Demand Response Programs on the Operation of Electricity and Gas Systems." Energies 15, no. 6 (March 15, 2022): 2144. http://dx.doi.org/10.3390/en15062144.
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With increasing attention to climate change, the penetration level of renewable energy sources (RES) in the electricity network is increasing. Due to the intermittency of RES, gas-fired power plants could play a significant role in backing up the RES in order to maintain the supply–demand balance. As a result, the interaction between gas and power networks are significantly increasing. On the other hand, due to the increase in peak demand (e.g., electrification of heat), network operators are willing to execute demand response programs (DRPs) to improve congestion management and reduce costs. In this context, modeling and optimal implementation of DRPs in proportion to the demand is one of the main issues for gas and power network operators. In this paper, an emergency demand response program (EDRP) is implemented locally to reduce the congestion of transmission lines and gas pipelines more efficiently. Additionally, the effects of optimal implementation of local emergency demand response program (LEDRP) in gas and power networks using linear and non-linear economic models (power, exponential and logarithmic) for EDRP in terms of cost and line congestion and risk of unserved demand are investigated. The most reliable demand response model is the approach that has the least difference between the estimated demand and the actual demand. Furthermore, the role of the LEDRP in the case of hydrogen injection instead of natural gas in the gas infrastructure is investigated. The optimal incentives for each bus or node are determined based on the power transfer distribution factor, gas transfer distribution factor, available electricity or gas transmission capability, and combination of unit commitment with the LEDRP in the integrated operation of these networks. According to the results, implementing the LEDRP in gas and power networks reduces the total operation cost up to 11% and could facilitate hydrogen injection to the network. The proposed hybrid model is implemented on a 24-bus IEEE electricity network and a 15-bus gas network to quantify the role and value of different LEDRP models.
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Tiwari, Amit, Adarsh Dhar Dubey, and Devesh Patel. "Comparative Study of Short Term Load Forecasting Using Multilayer Feed Forward Neural Network With Back Propagation Learning and Radial Basis Functional Neural Network." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 7, no. 01 (June 25, 2015): 09–18. http://dx.doi.org/10.18090/samriddhi.v7i1.3307.
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The term load forecast refers to the projected load requirement using systematic process of defining load in sufficient quantitative detail so that important power system expansion decisions can be made. Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling and for system security such as peak load shaving by power interchange with interconnected utilities. With structural changes to electricity in recent years, there is an emphasis on Short Term Load Forecasting (STLF).STLF is the essential part of power system planning and operation. Basic operating functions such as unit commitment, economic dispatch, and fuel scheduling and unit maintenance can be performed efficiently with an accurate forecast. Short term load forecasting can help to estimate load flow and to make decisions that can prevent overloading. Timely implementations of such decisions lead to improvement of network reliability and to the reduced occurrences of equipment failures and blackouts. The aim of short term load forecasting is to predict future electricity demands based, traditionally on historical data and predicted weather conditions. Short term load forecasting in its basic form is a statistical problem, where in the previous load values (time series variables) and influencing factors (casual variables) are used to determine the future loads.
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Saboori, Hedayat, Shahram Jadid, and Mehdi Savaghebi. "Optimal Management of Mobile Battery Energy Storage as a Self-Driving, Self-Powered and Movable Charging Station to Promote Electric Vehicle Adoption." Energies 14, no. 3 (January 31, 2021): 736. http://dx.doi.org/10.3390/en14030736.
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The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery charging are the main factors affecting the increased adoption of EVs. The installation location of fixed charging stations (FCSs) may not be completely compatible with the changing pattern of EV accumulation. Besides, their power withdrawal location in the network is fixed, and also, the time of receiving the power follows the EVs’ charging demand. The EV charging demand pattern conflicts with the network peak period and causes several technical challenges besides high electricity prices for charging. A mobile battery energy storage (MBES) equipped with charging piles can constitute a mobile charging station (MCS). The MCS has the potential to target the challenges mentioned above through a spatio-temporal transfer in the required energy for EV charging. Accordingly, in this paper, a new method for modeling and optimal management of mobile charging stations in power distribution networks in the presence of fixed stations is presented. The MCS is powered through its internal battery utilizing a self-powered mechanism. Besides, it employs a self-driving mechanism for lowering transportation costs. The MCS battery can receive the required energy at a different time and location regarding EVs accumulation and charging demand pattern. In other words, the mobile station will be charged at the most appropriate location and time by moving between the network buses. The stored energy will then be used to charge the EVs in the fixed stations’ vicinity at peak EV charging periods. In this way, the energy required for EV charging will be stored during off-peak periods, without stress on the network and at the lowest cost. Implementing the proposed method on a test case demonstrates its benefits for both EV owners and network operator.
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Koch, Katharina, Bastian Alt, and Matthias Gaderer. "Dynamic Modeling of a Decarbonized District Heating System with CHP Plants in Electricity-Based Mode of Operation." Energies 13, no. 16 (August 10, 2020): 4134. http://dx.doi.org/10.3390/en13164134.
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The targets of global CO2 reduction outline the importance of decarbonizing the heating and cooling sector, which consume half of the final energy in the European Union (EU). Consequently, heating network operators must adapt to growing requirements for carbon neutrality. Energy system modeling allows the simulation of individual network compositions and regulations, while considering electricity market signals for a more efficient plant operation. The district heating model, programmed for this work, covers a measured heat demand with peak load boiler, biomass-fired combined heat and power (CHP) plant, and biomass heating plant supply. The CHP plant reacts to electricity prices of the European Power Exchange market and uses a long-term heat storage to decouple heat and electricity production. This paper presents the results of three annual simulation scenarios aimed at carbon neutrality for the analyzed heating network. Two scenarios achieve a climate-neutral system by replacing the peak load boiler generation. The exclusive storage capacity expansion in the first scenario does not lead to the intended decarbonization. The second scenario increases the output of the CHP plant, while the third simulation uses the biomass heating plant supply. This additional heat producer enables a significant reduction in storage capacity and a higher CHP plant participation in the considered electricity market.
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Valinejad, Jaber, Taghi Barforoshi, Mousa Marzband, Edris Pouresmaeil, Radu Godina, and João P. S. Catalão. "Investment Incentives in Competitive Electricity Markets." Applied Sciences 8, no. 10 (October 18, 2018): 1978. http://dx.doi.org/10.3390/app8101978.
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This paper presents the analysis of a novel framework of study and the impact of different market design criterion for the generation expansion planning (GEP) in competitive electricity market incentives, under variable uncertainties in a single year horizon. As investment incentives conventionally consist of firm contracts and capacity payments, in this study, the electricity generation investment problem is considered from a strategic generation company (GENCO) ′ s perspective, modelled as a bi-level optimization method. The first-level includes decision steps related to investment incentives to maximize the total profit in the planning horizon. The second-level includes optimization steps focusing on maximizing social welfare when the electricity market is regulated for the current horizon. In addition, variable uncertainties, on offering and investment, are modelled using set of different scenarios. The bi-level optimization problem is then converted to a single-level problem and then represented as a mixed integer linear program (MILP) after linearization. The efficiency of the proposed framework is assessed on the MAZANDARAN regional electric company (MREC) transmission network, integral to IRAN interconnected power system for both elastic and inelastic demands. Simulations show the significance of optimizing the firm contract and the capacity payment that encourages the generation investment for peak technology and improves long-term stability of electricity markets.
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Town, Graham, Seyedfoad Taghizadeh, and Sara Deilami. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning." Energies 15, no. 4 (February 10, 2022): 1276. http://dx.doi.org/10.3390/en15041276.
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As the number and range of electric vehicles in use increases, and the size of batteries in those vehicles increases, the demand for fast and ultra-fast charging infrastructure is also expected to increase. The growth in the fast charging infrastructure raises a number of challenges to be addressed; primarily, high peak loads and their impacts on the electricity network. This paper reviews fast and ultra-fast charging technology and systems from a number of perspectives, including the following: current and expected trends in fast charging demand; the particular temporal and spatial characteristics of electricity demand associated with fast charging; the devices and circuit technologies commonly used in fast chargers; the potential system impacts of fast charging on the electricity distribution network and methods for managing those impacts; methods for long-term planning of fast charging facilities; finally, expected future developments in fast charging technology and systems.
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Wan, Zhengdong, Yan Huang, Liangzheng Wu, and Chengwei Liu. "ADPA Optimization for Real-Time Energy Management Using Deep Learning." Energies 17, no. 19 (September 26, 2024): 4821. http://dx.doi.org/10.3390/en17194821.
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The current generation of renewable energy remains insufficient to meet the demands of users within the network, leading to the necessity of curtailing flexible loads and underscoring the urgent need for optimized microgrid energy management. In this study, the deep learning-based Adaptive Dynamic Programming Algorithm (ADPA) was introduced to integrate real-time pricing into the optimization of demand-side energy management for microgrids. This approach not only achieved a dynamic balance between supply and demand, along with peak shaving and valley filling, but it also enhanced the rationality of energy management strategies, thereby ensuring stable microgrid operation. Simulations of the Real-Time Electricity Price (REP) management model under demand-side response conditions validated the effectiveness and feasibility of this approach in microgrid energy management. Based on the deep neural network model, optimization of the objective function was achieved with merely 54 epochs, suggesting a highly efficient computational process. Furthermore, the integration of microgrid energy management with the REP conformed to the distributed multi-source power supply microgrid energy management and scheduling and improved the efficiency of clean energy utilization significantly, supporting the implementation of national policies aimed at the development of a sustainable power grid.
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Chen, Wen, Chun Lin Guo, Zong Feng Li, Dong Ming Jia, Jun Chen, Xiang Zhen Li, Guo Zhong Zhuang, and Zhu Liu. "Research of Time-of-Use Tariff Considering Electric Vehicles Charging Demands." Advanced Materials Research 953-954 (June 2014): 1354–58. http://dx.doi.org/10.4028/www.scientific.net/amr.953-954.1354.
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With the large-scale EV(electric vehicle) integrating into the power system, new challenges has been brought to the planning as well as the security of the network. There will be a great impact on the system if the system operator ignores the vast quantity of EV charging at the same. Thus, taking measures, e.g. the multiple tariff, is of vital importance to give the guidance to the EV owners to charging wisely to save the daily cost on charging, as well as reduce the gap between peak load and valley load. A model for TOU has been presented in this paper. In the model , an objective function is declared to describe the purpose of TOU, and the optimal solution is gained according to the response of EV when the price of electricity changes. Finally , a case based on the daily load curve of a certain place is calculated with the model in this paper.
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Komen, V., A. Antonić, T. Barićević, M. Skok, and T. Dolenc. "Coordinated TSO and DSO network development plan on the islands of Cres and Lošinj." Journal of Energy - Energija 67, no. 3 (June 2, 2022): 45–50. http://dx.doi.org/10.37798/201867374.
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The paper presents an example of coordinated transmission and distribution network planning based on analyses conducted as part of the study on long term distribution network development plan for islands of Cres and Lošinj in Croatia. The observed area of two large and several smaller islands is supplied with electricity by one long radial 110 kV TSO owned line and parallel radial 35 kV DSO owned line. Due to transmission capacity of 35 kV line limited to 40% of the area peak demand, which is highly conditioned by tourism, the (N-1) criteria is not complied with in case of unavailability of 110 kV line during the two-month period in summer high season. Construction of the second 110 kV line as a common solution is extremely costly, due to necessity of laying down several kilometres of submarine cables. The paper provides the cost benefit analyses of this basic scenario and other possible alternative scenarios, including also investments in DSO network, to determine the most cost-effective solution. Due to the values of the demands and networks lengths, the presented example is close to a worst case scenario concerning the reliability of supply requirement, requesting thus some atypical distribution network analyses, elements and even conducted field tests of operation. The results clearly show that coordination of TSO and DSO planning is beneficiary concerning efficiency of investments in the networks. However, further analyses are recommended presuming contribution to satisfying the (N-1) criteria by use of non-traditional (“non-network” or “third party”) solutions.
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Roldán-Blay, Carlos, Vladimiro Miranda, Leonel Carvalho, and Carlos Roldán-Porta. "Optimal Generation Scheduling with Dynamic Profiles for the Sustainable Development of Electricity Grids." Sustainability 11, no. 24 (December 11, 2019): 7111. http://dx.doi.org/10.3390/su11247111.
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The integration of renewable generation in electricity networks is one of the most widespread strategies to improve sustainability and to deal with the energy supply problem. Typically, the reinforcement of the generation fleet of an existing network requires the assessment and minimization of the installation and operating costs of all the energy resources in the network. Such analyses are usually conducted using peak demand and generation data. This paper proposes a method to optimize the location and size of different types of generation resources in a network, taking into account the typical evolution of demand and generation. The importance of considering this evolution is analyzed and the methodology is applied to two standard networks, namely the Institute of Electrical and Electronics Engineers (IEEE) 30-bus and the IEEE 118-bus. The proposed algorithm is based on the use of particle swarm optimization (PSO). In addition, the use of an initialization process based on the cross entropy (CE) method to accelerate convergence in problems of high computational cost is explored. The results of the case studies highlight the importance of considering dynamic demand and generation profiles to reach an effective integration of renewable resources (RRs) towards a sustainable development of electric systems.
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Yotto, Habib Conrad Sotiman, Patrice Chetangny, Victor Zogbochi, Jacques Aredjodoun, Sossou Houndedako, Gerald Barbier, Antoine Vianou, and Didier Chamagne. "Long-Term Electricity Load Forecasting Using Artificial Neural Network: The Case Study of Benin." Advanced Engineering Forum 48 (January 10, 2023): 117–36. http://dx.doi.org/10.4028/p-zq4id8.
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Africans in general and specially Beninese’s low rate access to electricity requires efforts to set up new electricity production units. To satistfy the needs, it is therefore very important to have a prior knowledge of the electrical load. In this context, knowing the right need for the electrical energy to be extracted from the Beninese network in the long term and in order to better plan its stability and reliability, a forecast of this electrical load is then necessary. The study has used the annual power grid peak demand data from 2001 to 2020 to develop, train and validate the models. The electrical load peaks until 2030 are estimated as the output value. This article evaluates three algorithms of a method used in artificial neural networks (ANN) to predict electricity consumption, which is the Multilayer Perceptron (MLP) with backpropagation. To ensure stable and accurate predictions, an evaluation approach using mean square error (MSE) and correlation coefficient (R) has been used. The results have proved that the data predicted by the Bayesian regulation variant of the Multilayer Perceptron (MLP), is very close to the real data during the training and the learning of these algorithms. The validated model has developed high generalization capabilities with insignificant prediction deviations.
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Saberi Derakhtenjani, Ali, and Andreas K. Athienitis. "Model Predictive Control Strategies to Activate the Energy Flexibility for Zones with Hydronic Radiant Systems." Energies 14, no. 4 (February 23, 2021): 1195. http://dx.doi.org/10.3390/en14041195.
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This paper presents control strategies to activate energy flexibility for zones with radiant heating systems in response to changes in electricity prices. The focus is on zones with radiant floor heating systems for which the hydronic pipes are located deep in the concrete and, therefore, there is a significant thermal lag. A perimeter zone test-room equipped with a hydronic radiant floor system in an environmental chamber is used as a case study. A low order thermal network model for the perimeter zone, validated with experimental measurements, is utilized to study various control strategies in response to changes in the electrical grid price signal, including short term (nearly reactive) changes of the order of 10–15 min notice. An index is utilized to quantify the building energy flexibility with the focus on peak demand reduction for specific periods of time when the electricity prices are higher than usual. It is shown that the developed control strategies can aid greatly in enhancing the zone energy flexibility and minimizing the cost of electricity and up to 100% reduction in peak power demand and energy consumption is attained during the high-price and peak-demand periods, while maintaining acceptable comfort conditions.
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Lewis, Jim, Kerrie Mengersen, Laurie Buys, Desley Vine, John Bell, Peter Morris, and Gerard Ledwich. "Systems Modelling of the Socio-Technical Aspects of Residential Electricity Use and Network Peak Demand." PLOS ONE 10, no. 7 (July 30, 2015): e0134086. http://dx.doi.org/10.1371/journal.pone.0134086.
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Alhasnawi, Bilal Naji, Basil H. Jasim, Pierluigi Siano, Hassan Haes Alhelou, and Amer Al-Hinai. "A Novel Solution for Day-Ahead Scheduling Problems Using the IoT-Based Bald Eagle Search Optimization Algorithm." Inventions 7, no. 3 (June 23, 2022): 48. http://dx.doi.org/10.3390/inventions7030048.
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Advances in technology and population growth are two factors responsible for increasing electricity consumption, which directly increases the production of electrical energy. Additionally, due to environmental, technical and economic constraints, it is challenging to meet demand at certain hours, such as peak hours. Therefore, it is necessary to manage network consumption to modify the peak load and tackle power system constraints. One way to achieve this goal is to use a demand response program. The home energy management system (HEMS), based on advanced internet of things (IoT) technology, has attracted the special attention of engineers in the smart grid (SG) field and has the tasks of demand-side management (DSM) and helping to control equality between demand and electricity supply. The main performance of the HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic called the bald eagle search optimization algorithm (BESOA) to discover the optimal scheduling of home appliances. Furthermore, the HEMS architecture is programmed based on MATLAB and ThingSpeak modules. The HEMS uses the BESOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the PAR, and increase user comfort. The results show the suggested system’s ability to obtain optimal home energy management, decreasing the energy cost, microgrid emission cost, and PAR (peak to average ratio).
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Rajesh, C. R., and Aravind S. P. "Demand side management in electric vehicles for smartcharging and discharging based on state aggregationand q-learning." BOHR Journal of Electrical & Electronic Communication Engineering 1, no. 1 (2024): 1–5. http://dx.doi.org/10.54646/bjeece.2024.01.
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Use of traditional energy sources results in significant pollution. International organizations are making many efforts to reduce emissions of carbon dioxide (CO2). According to research, EVs can reduce CO2emissions by28% by the year 2030. However, the prohibitive price of EVs and the scarcity of outlets for charging continue to be two of the biggest barriers to the widespread use of electric vehicles. In this paper, a detailed demand-side management approach for a network-connected, solar-powered electric vehicle charging station is provided. The proposed approach reduces the requirement for conventional power sources and addresses the current problem of insufficient EVCS by using a solar-powered EVCS in order to make up for the electricity used during peak demand. Models for PV power plants, industrial loads, residential loads, and charging stations for electric vehicles were created using the real-time data. Additionally, a method based on deep learning was devised to control the microgrid’s supply of electricity and to recharge the battery of the electric vehicle during off-peak hours. Two alternative machine learning techniques for figuring out the level of charge in a device that stores electricity were also put to the test. Finally, a 24-h case study using the suggested demand management system structure was conducted. The data show that peak consumption is offset by using a charging station for electric vehicles during peak periods.
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Lai, Jingang, Hong Zhou, Wenshan Hu, Dongguo Zhou, and Liang Zhong. "Smart Demand Response Based on Smart Homes." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/912535.
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Smart homes (SHs) are crucial parts for demand response management (DRM) of smart grid (SG). The aim of SHs based demand response (DR) is to provide a flexible two-way energy feedback whilst (or shortly after) the consumption occurs. It can potentially persuade end-users to achieve energy saving and cooperate with the electricity producer or supplier to maintain balance between the electricity supply and demand through the method of peak shaving and valley filling. However, existing solutions are challenged by the lack of consideration between the wide application of fiber power cable to the home (FPCTTH) and related users’ behaviors. Based on the new network infrastructure, the design and development of smart DR systems based on SHs are related with not only functionalities as security, convenience, and comfort, but also energy savings. A new multirouting protocol based on Kruskal’s algorithm is designed for the reliability and safety of the SHs distribution network. The benefits of FPCTTH-based SHs are summarized at the end of the paper.