Artykuły w czasopismach: „Electricity network and pricing”

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G.Umale, V., Dr S.B.Warkad, S. Wadhankar i H. S.Sakhare. "Optimal Pricing of Electricity in Restructured Electricity Market". International Journal of Engineering & Technology 7, nr 4.5 (22.09.2018): 389. http://dx.doi.org/10.14419/ijet.v7i4.5.20189.

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An effective pricing scheme that to provide the useful information to generation, transmission section and customers. These transmission pricing depends on generator, load levels and transmission line constraints. Transmission line constraints result is variations in energy prices throughout the network. The proposed approach is based on AC-DC optimal power flow model with considering of losses. Resulting optimization problem is solved by linear programming approach. Locational Marginal Pricing methodology is used to determine the energy price for transacted power and to manage the network congestion and marginal losses. Variation of LMP values with transmission constraint conditions also studied. Simulation is carried out on IEEE 57 bus system, 400/765kV MSETCL system of Maharashtra transmission line for real data bus system and the results are presented.

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Morais, M. S., i J. W. Marangon Lima. "Combined natural gas and electricity network pricing". Electric Power Systems Research 77, nr 5-6 (kwiecień 2007): 712–19. http://dx.doi.org/10.1016/j.epsr.2006.05.005.

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Gökgöz, Fazil, i Fahrettin Filiz. "Electricity price forecasting in Turkey with artificial neural network models". Investment Management and Financial Innovations 13, nr 3 (23.09.2016): 150–58. http://dx.doi.org/10.21511/imfi.13(3-1).2016.01.

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The electricity market has experienced significant changes towards deregulation with the aim of improving economic efficiency. The electricity pricing is a major consideration for consumers and generation companies in deregulated electric markets, so that offering the right price for electricity has become more important. Various methods and ideas have been tried for electricity price forecasting. Artificial neural networks have received much attention with its nonlinear property and many papers have reported successful experiments with them. This paper introduces artificial neural network models for day-ahead electricity market in Turkey. Using gradient descent, gradient descent with momentum, Broydan, Fletcher, Goldfarb and Shanno (BFGS) and Levenberg-Marquardt algorithm with different number of neuron and transfer functions, 400 different models are created. Performances of different models are compared according to their Mean Absolute Percentage (MAPE) values; the most successful models MAPE value is observed as 9.76%. Keywords: electricity price forecasting, neural networks, day-ahead electricity market, Turkey. JEL Classification: C02, C13, C45, C53

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Rokamwar, Kaustubh. "Feed- Forward Neural Network based Day Ahead Nodal Pricing". International Journal for Research in Applied Science and Engineering Technology 9, nr VII (15.07.2021): 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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Joskow, Paul L. "Creating a Smarter U.S. Electricity Grid". Journal of Economic Perspectives 26, nr 1 (1.02.2012): 29–48. http://dx.doi.org/10.1257/jep.26.1.29.

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This paper focuses on efforts to build what policymakers call the “smart grid,” involving 1) improved remote monitoring and automatic and remote control of facilities in high-voltage electricity transmission networks; 2) improved remote monitoring, two-way communications, and automatic and remote control of local distribution networks; and 3) installation of “smart” metering and associated communications capabilities on customer premises so that customers can receive real-time price information and/or take advantage of opportunities to contract with their retail supplier to manage the consumer's electricity demands remotely in response to wholesale prices and network congestion. I examine the opportunities, challenges, and uncertainties associated with investments in “smart grid” technologies. I discuss some basic electricity supply and demand, pricing, and physical network attributes that are critical for understanding the opportunities and challenges associated with expanding deployment of smart grid technologies. Then I cover issues associated with the deployment of these technologies at the high voltage transmission, local distribution, and end-use metering levels.

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Liu, Xu, i Yang Liu. "A Transmission Pricing Method Considering Long-Term Capacity Cost". Applied Mechanics and Materials 316-317 (kwiecień 2013): 128–31. http://dx.doi.org/10.4028/www.scientific.net/amm.316-317.128.

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In the research of electricity market, electricity pricing is a key issue. Transmission pricing affects the interests of generation company, transmission company and consumer. In this paper, a new method of transmission pricing is proposed .It is based on the short term marginal cost method and further considers capacity cost. Simulation results show that the method proposed can not only lead to short-run market efficiency by providing effective economic signals to generators and consumers, but also ensure the balance between income and expenditure of transmission companies as well as help them accumulate special fund for transmission network expansion.

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Kobayashi, Koichi, i Kunihiko Hiraishi. "A Probabilistic Approach to Control of Complex Systems and Its Application to Real-Time Pricing". Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/906717.

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Control of complex systems is one of the fundamental problems in control theory. In this paper, a control method for complex systems modeled by a probabilistic Boolean network (PBN) is studied. A PBN is widely used as a model of complex systems such as gene regulatory networks. For a PBN, the structural control problem is newly formulated. In this problem, a discrete probability distribution appeared in a PBN is controlled by the continuous-valued input. For this problem, an approximate solution method using a matrix-based representation for a PBN is proposed. Then, the problem is approximated by a linear programming problem. Furthermore, the proposed method is applied to design of real-time pricing systems of electricity. Electricity conservation is achieved by appropriately determining the electricity price over time. The effectiveness of the proposed method is presented by a numerical example on real-time pricing systems.

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Sidnell, Tim, Bogdan Dorneanu, Evgenia Mechleri, Vassilios S. Vassiliadis i Harvey Arellano-Garcia. "Effects of Dynamic Pricing on the Design and Operation of Distributed Energy Resource Networks". Processes 9, nr 8 (28.07.2021): 1306. http://dx.doi.org/10.3390/pr9081306.

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This paper presents a framework for the use of variable pricing to control electricity imported/exported to/from both fixed and unfixed residential distributed energy resource (DER) network designs. The framework shows that networks utilizing much of their own energy, and importing little from the national grid, are barely affected by dynamic import pricing, but are encouraged to sell more by dynamic export pricing. An increase in CO2 emissions per kWh of energy produced is observed for dynamic import and export, against a baseline configuration utilizing constant pricing. This is due to feed-in tariffs (FITs) that encourage CHP generation over lower-carbon technologies. Furthermore, batteries are shown to be expensive in systems receiving income from FITs and grid exports, but for the cases when they sell to/buy from the grid using dynamic pricing, their use in the networks becomes more economical.

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Umale, Virendra, i Sanjay Warkad. "Power quality based optimal nodal pricing in tradable electricity market". International Journal of Engineering & Technology 7, nr 2.8 (19.03.2018): 692. http://dx.doi.org/10.14419/ijet.v7i2.8.10560.

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Optimal Power Flow method described the nodal transmission pricing into different related factors, such as congestion,generation, power and electric load limitations. These detailsof each bus transmission prices can be used for to improve the proper usage of transmission congestion and power grid and to get reasonable transmission pricing for power structure. The proposed methodology is demonstrated on IEEE57 bus system and Maharashtra utility electric 400/765kv network.

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Soonee, S. K., S. S. Barpanda, Mohit Joshi, Nripen Mishra i Vaishally Bhardwaj. "Point of Connection Transmission Pricing in India". International Journal of Emerging Electric Power Systems 14, nr 1 (30.05.2013): 9–16. http://dx.doi.org/10.1515/ijeeps-2013-0027.

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Abstract The National Electricity Policy (NEP) [1], issued by the Government of India, mandates transmission prices to be distance and direction sensitive and capture utilization of the network by each network user. In line with the mandate, the Central Electricity Regulatory Commission (CERC) [2] has issued Sharing of Interstate Transmission Charges and Losses Regulations, 2010 [3], to introduce point of connection (PoC)-based transmission pricing methodology in India. The methodology under the above regulations introduces one of the major reforms of its kind in the Indian power sector and seeks to share the total transmission charges in proportion to respective utilization of the transmission system by different entities. In this paper, the authors have enumerated their experience gained from the implementation of PoC-based transmission pricing regime in India. Authors have also discussed various issues encountered in the process of implementation and the methodology adopted.

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Amin, Adil, Wajahat Ullah Khan Tareen, Muhammad Usman, Haider Ali, Inam Bari, Ben Horan, Saad Mekhilef, Muhammad Asif, Saeed Ahmed i Anzar Mahmood. "A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network". Sustainability 12, nr 23 (4.12.2020): 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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Kobayashi, Koichi, i Kunihiko Hiraishi. "Optimal Control of Probabilistic Logic Networks and Its Application to Real-Time Pricing of Electricity". Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/952310.

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In analysis and control of large-scale complex systems, a discrete model plays an important role. In this paper, a probabilistic logic network (PLN) is considered as a discrete model. A PLN is a mathematical model where multivalued logic functions are randomly switched. For a PLN with two kinds of control inputs, the optimal control problem is formulated, and an approximate solution method for this problem is proposed. In the proposed method, using a matrix-based representation for a PLN, this problem is approximated by a mixed integer linear programming problem. In application, real-time pricing of electricity is studied. In real-time pricing, electricity conservation is achieved by setting a high electricity price. A numerical example is presented to show the effectiveness of the proposed method.

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Dino, Giuseppe Edoardo, Pietro Catrini, Valeria Palomba, Andrea Frazzica i Antonio Piacentino. "Promoting the Flexibility of Thermal Prosumers Equipped with Heat Pumps to Support Power Grid Management". Sustainability 15, nr 9 (3.05.2023): 7494. http://dx.doi.org/10.3390/su15097494.

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The increasing share of renewable energy sources in energy systems will lead to unpredictable moments of surplus/deficit in energy production. To address this issue, users with heat pumps can provide support to power grid operators through flexible unit operation achieved via Demand Response programs. For buildings connected to low-temperature heating networks with ensured third-party access, further room for flexibility can be explored by investigating the production of surplus heat that can be sold to the network. A key aspect lies in the identification of the energy pricing options that could encourage such flexible operation of a heat pump by “thermal prosumers”. To this aim, the present study investigates the impact of ad hoc variations in the electricity purchasing price through discounts or penalties included in the “network cost” component of the price on cost-effective operation of a heat pump connected to the thermal network. To discuss the effects of different pricing options in terms of increased flexibility, an office building located in Italy and equipped with a high-temperature heat pump is adopted as the case study. A heuristic profit-oriented management strategy of the heat pump is assumed, and dynamic simulations are performed. The results indicate that at current electricity prices, the heat pump operation is profitable both when supplying the heat to meet the building’s requirements and when producing surplus heat for sale to the thermal network. In addition, it is revealed that the penalties applied to the electricity purchasing price are effective in encouraging changes in the heat pump operation strategy, reducing its average production (the building increasingly relying on buying heat from the network) and the associated electricity consumption by 46.0% and 79.7% in the “light” and “severe” local power deficit scenarios, respectively.

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Tushar, Mosaddek Hossain Kamal, i Chadi Assi. "Optimal Energy Management and Marginal-Cost Electricity Pricing in Microgrid Network". IEEE Transactions on Industrial Informatics 13, nr 6 (grudzień 2017): 3286–98. http://dx.doi.org/10.1109/tii.2017.2712652.

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Arroyo, J. M., i F. D. Galiana. "Energy and Reserve Pricing in Security and Network-Constrained Electricity Markets". IEEE Transactions on Power Systems 20, nr 2 (maj 2005): 634–43. http://dx.doi.org/10.1109/tpwrs.2005.846221.

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Rouhani, Anise, Habib Rajabi Mashhadi i Mehdi Feizi. "Estimating the Short-term Price Elasticity of Residential Electricity Demand in Iran". International Transactions on Electrical Energy Systems 2022 (13.08.2022): 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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Lin, Jun-Lin, Yiqing Zhang, Kunhuang Zhu, Binbin Chen i Feng Zhang. "Asymmetric Loss Functions for Contract Capacity Optimization". Energies 13, nr 12 (16.06.2020): 3123. http://dx.doi.org/10.3390/en13123123.

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For high-voltage and extra-high-voltage consumers, the electricity cost depends not only on the power consumed but also on the contract capacity. For the same amount of power consumed, the smaller the difference between the contract capacity and the power consumed, the smaller the electricity cost. Thus, predicting the future power demand for setting the contract capacity is of great economic interest. In the literature, most works predict the future power demand based on a symmetric loss function, such as mean squared error. However, the electricity pricing structure is asymmetric to the under- and overestimation of the actual power demand. In this work, we proposed several loss functions derived from the asymmetric electricity pricing structure. We experimented with the Long Short-Term Memory neural network with these loss functions using a real dataset from a large manufacturing company in the electronics industry in Taiwan. The results show that the proposed asymmetric loss functions outperform the commonly used symmetric loss function, with a saving on the electricity cost ranging from 0.88% to 2.42%.

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Gadge, Pramod, i Prakash Burade. "Optimal Spot Pricing Evaluation in Restructured Electrical Power System". International Journal of Electrical and Electronics Research 10, nr 4 (30.12.2022): 1021–26. http://dx.doi.org/10.37391/ijeer.100444.

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Electricity markets in both developed and developing countries have been considerably reorganized over the previous two decades. The unbundling of generation and transmission results in restructured electricity market which enhances the competition among the market traders in the regime of open access. Therefore, the transmission pricing methods should be skilled in translating transmission costs into tariffs to allow participation, which leads to profitable effectiveness, allows the grid owner to recover prices, and makes market participants aware of the system's supply defense and consistency maintained. This research intends to (1) examine the drivers behind power transmission pricing, (2) Create AC-DC OPF-based Nodal Pricing methodology, and (3) work out prices for India’s real transmission network to enable market participants to compete and make the best decisions. Finally, the study indicated that the planned methodology is more appropriate for rising nations to achieve the goals of building extensive energy markets.

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Weibelzahl, Martin. "Nodal, zonal, or uniform electricity pricing: how to deal with network congestion". Frontiers in Energy 11, nr 2 (30.03.2017): 210–32. http://dx.doi.org/10.1007/s11708-017-0460-z.

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Pao, Hsiao-Tien. "Forecasting electricity market pricing using artificial neural networks". Energy Conversion and Management 48, nr 3 (marzec 2007): 907–12. http://dx.doi.org/10.1016/j.enconman.2006.08.016.

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Khalid, Zubair, Ghulam Abbas, Muhammad Awais, Thamer Alquthami i Muhammad Babar Rasheed. "A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid". Energies 13, nr 5 (29.02.2020): 1062. http://dx.doi.org/10.3390/en13051062.

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In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company.

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Zhang, Jing, Xiangpeng Zhan, Taoyong Li, Linru Jiang, Jun Yang, Yuanxing Zhang, Xiaohong Diao i Sining Han. "A Convex Optimization Algorithm for Electricity Pricing of Charging Stations". Algorithms 12, nr 10 (1.10.2019): 208. http://dx.doi.org/10.3390/a12100208.

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The problem of electricity pricing for charging stations is a multi-objective mixed integer nonlinear programming. Existing algorithms have low efficiency in solving this problem. In this paper, a convex optimization algorithm is proposed to get the optimal solution quickly. Firstly, the model is transformed into a convex optimization problem by second-order conic relaxation and Karush–Kuhn–Tucker optimality conditions. Secondly, a polyhedral approximation method is applied to construct a mixed integer linear programming, which can be solved quickly by branch and bound method. Finally, the model is solved many times to obtain the Pareto front according to the scalarization basic theorem. Based on an IEEE 33-bus distribution network model, simulation results show that the proposed algorithm can obtain an exact global optimal solution quickly compared with the heuristic method.

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Shu, Zhe, Shan Mei i Wei Ping Wang. "The Game-Theoretic Approach to Pricing in China's Semi-Deregulated Electricity Market". Advanced Materials Research 805-806 (wrzesień 2013): 1116–21. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.1116.

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In Chinas semi-deregulated electricity market, a game-theoretic model has been set between users and power suppliers in order to ensure the whole network voltage load curve stay smooth, as well as to make the suppliers profitable on the basis of the satisfaction of users in power utilization in daily lives. The foundation of the model is the definition of the price elasticity of demand and its characters in micro-economy. According to this model, a Bayesian Nash equilibrium has been put forward based on the demand price elasticity, which can not only smooth the whole network voltage load curve effectively through the reciprocal process between users and power suppliers, but also can optimize the profit of the power suppliers. The simulation results of the voltage load curves and price changes graphs show the effectiveness of modeling simulation.

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Salazar, Eduardo J., Mauro Jurado i Mauricio E. Samper. "Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids". Energies 16, nr 3 (2.02.2023): 1466. http://dx.doi.org/10.3390/en16031466.

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International agreements support the modernization of electricity networks and renewable energy resources (RES). However, these RES affect market prices due to resource variability (e.g., solar). Among the alternatives, Demand Response (DR) is presented as a tool to improve the balance between electricity supply and demand by adapting consumption to available production. In this sense, this work focuses on developing a DR model that combines price and incentive-based demand response models (P-B and I-B) to efficiently manage consumer demand with data from a real San Juan—Argentina distribution network. In addition, a price scheme is proposed in real time and by the time of use in relation to the consumers’ influence in the peak demand of the system. The proposed schemes increase load factor and improve demand displacement compared to a demand response reference model. In addition, the proposed reinforcement learning model improves short-term and long-term price search. Finally, a description and formulation of the market where the work was implemented is presented.

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Luhangala, Douglas Logedi, Amollo Ambole, Josephine Kaviti Musango, Fabrizio Ceschin i Simeon Dulo. "Energy price modeling in sub-Saharan Africa: a systematic literature review". Environmental Research: Infrastructure and Sustainability 2, nr 1 (13.01.2022): 015001. http://dx.doi.org/10.1088/2634-4505/ac3fee.

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Abstract Researchers have found that despite a wide range of renewable energy sources in sub-Saharan Africa (SSA), renewable energy pricing policies have focused extensively on metered electricity energy, an early source of renewable energy. Supply, access, and regulation of price for metered electricity energy is mostly controlled by the governments across SSA. There is an increasing use of other renewable energy sources including portable electricity, solar power, and wind power. However, in SSA, the pricing for domestic renewable domestic renewable power such as portable electricity, rechargeable cookstoves, and portable solar power sources are left to the market to legislate, with energy prices dependent on forces of demand and supply and seldom on clear scientific models. This commercially focused energy market means businesses operating in the energy industry are more interested in profits and set prices relative to their market perceptions. The main problem with the energy market in SSA is the lack of a participatory approach where customers, businesses, the government, and other stakeholders are involved in the pricing for energy. We further note that lack of a participatory approach in energy pricing is a major challenge in uptake and demand for the domestic renewable energy sources. Through a systematic literature review, including a review of peer-reviewed journals, documents from energy utility companies, and published information on the websites for energy companies, this review analyzes the current application of energy price modeling and hypothesizes that mobile technology and a participatory pricing approach can improve pricing for domestic renewable power. Our initial literature review showed that energy price modeling had received little attention in SSA, especially for domestic renewable power energy sources. This paper, therefore, fills this gap by using a systematic literature review to consolidate knowledge on how energy price modeling has been applied in the SSA context. The systematic literature review results reveal four commonly used models: time series, artificial neural network, hybrid iterative reactive adaptive, and hybrid models. These energy pricing models are mainly applied to metered electricity power, the predominant source of energy in SSA. The literature hypothesizes that applying mobile technology to energy pricing and a participatory approach involving the consumers and energy supply businesses can move SSA closer to transitioning to renewable energy. Although other factors have hindered this transition, a participatory energy pricing approach incorporating relevant pricing models and market information creates potential solutions to these challenges. In the discussion, we hypothesize that a participatory approach to price modeling with the incorporation of mobile technology can be used at the household level to improve energy decision-making. For this to work, energy price modeling for domestic renewable sources should be simplified, user-friendly, and accessible to households. In conclusion, we recommend that SSA governments develop a more holistic view of energy price modeling to better harness the potential for domestic renewable energy sources.

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Yu, Shan Jin, Hui Yu Jin, Xiao Bin Tan i Kang Qi Wang. "An Economical Scheduling Strategy of Battery-Equipped Data Server with Dynamic-Pricing Power Supply in Smart Grid". Advanced Materials Research 869-870 (grudzień 2013): 426–31. http://dx.doi.org/10.4028/www.scientific.net/amr.869-870.426.

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The electricity consumption by modern data center and data servers has significantly increased in recent year and continues to have this dramatic increase trend. Meanwhile, more and more modern power grids have adopted dynamic pricing electricity supply model. When a data center or data server is equipped with temporary power storage devices such as a battery, it is feasible and important to study how to schedule power supply to reduce the overall power consumption cost. In this paper, we present a dynamic programming based scheduling strategy by considering the stochastic arrival nature of network load and characteristic of battery storage. We demonstrate the effectiveness of our approach using simulation based on real power price data and real-life network load data.

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Cai, Qinqin, Yongqiang Zhu, Xiaohua Yang i Lin E. "Alterable Electricity Pricing Mechanism Considering the Deviation of Wind Power Prediction". Sustainability 12, nr 5 (1.03.2020): 1848. http://dx.doi.org/10.3390/su12051848.

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Fluctuation and prediction errors of wind power would cause a large amount of automatic generation control (AGC) adjustment costs, which lead to the problem of power curtailment. A reasonable mechanism of grid-connection electricity price may encourage wind farms to take measures to reduce the deviation between output power and schedule power, which is helpful for source-network coordination and reducing wind power curtailment. An alterable electricity pricing mechanism considering wind power deviation rate is proposed. In each schedule cycle, electricity price is adjusted according to the deviation rate and its historical change trend. In this way, wind farms will be encouraged to configure energy storage to promote the accordance of wind output power with schedule power to the greatest extent. Given the statistical characteristic of prediction errors of wind power, this paper proposes a schedule power model, taking least squares of output power deviation as objective function, and then puts forward an engineering application method for determining schedule power. This paper analyzes the overall cost and revenue of a wind farm to configure energy storage and determine the optimal energy storage capacity with the goal of maximizing the profit of the wind farm. In the case analysis, the effect of the deviation rate and its historical change trend, the deviation rate tolerance coefficient on electricity price is analyzed. The case analysis demonstrates the effectiveness of the proposed alterable electricity pricing mechanism and shows that the mechanism is helpful at reducing wind power output deviation and wind curtailment.

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Kolosok, S., i T. Vasylieva. "ANALYSIS OF GAS AND ELECTRICITY DISTRIBUTION NETWORKS: THE TARIFF REGULATION REVIEW". Vìsnik Sumsʹkogo deržavnogo unìversitetu, nr 2 (2020): 74–78. http://dx.doi.org/10.21272/1817-9215.2020.2-8.

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The distribution of gas and electricity certainly belongs to the strategically important activities, the success of which affects the socio-economic situation in the country. Energy distribution companies not only transport energy to customers, but also balance energy consumption, thus influencing all economic processes. However, the energy sector is characterized by several limiting factors. Companies should optimize their activities through energy supply and reception planning, capacity forecasting, providing the necessary level of flexibility of energy systems and the ability to integrate diversified gas and electricity distribution operators. All this requires a balanced and detailed approach to the formation of tariff policy, which takes into account the cost of maintenance and maintenance of energy networks, justification of the costs of business operations given the possible social response to rising final tariffs for gas and electricity. Therefore, the issue of tariff regulation in the energy sector requires a detailed study and analysis of best practices for setting tariffs for services for energy network operators. To this end, the study provided a review of the scientific literature on tariff regulation of gas and electricity distribution networks. The results of the study did not show significant elaboration of the topic but revealed only differences in views on optimal pricing for energy distribution networks in different countries.

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Nekhoroshikh, I. N., T. V. Dobrinova, A. Yu Anisimov i A. V. Zhaglovskaya. "World practice of managing electricity demand". Russian Journal of Industrial Economics 12, nr 3 (26.09.2019): 280–87. http://dx.doi.org/10.17073/2072-1633-2019-3-280-287.

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The article explores the complex world practice of managing the demand for electricity, which allows not only to reduce consumer spending, but also to increase economic efficiency, reduce the demand for power systems, and reduce the need for additional generating capacity and resources. accordingly, a reduction in carbon dioxide emissions. The authors of the article argue that the development and implementation of structural elements and the creation of mechanisms for determining market indicators, taking into account the pricing model nodes adopted in the wholesale market. Currently, there is interest in energy crises and the desire to increase the demand for energy resources for the construction of expensive generators of highpower and network infrastructures, and re using effective re-market mechanisms. Reducing the demand for electricity requires less cost for the efficient operation of generating facilities. Reducing electricity consumption can lead to lower electricity

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Singh, Shweta. "Artificial Neural Network Based Load Forecasting". International Journal for Research in Applied Science and Engineering Technology 10, nr 2 (28.02.2022): 1071–76. http://dx.doi.org/10.22214/ijraset.2022.40467.

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Abstract: In this report our point is to figure the power costs as precisely as conceivable by planning a half and half model and utilizing different blunder capacities to really take a look at the exactness of our outcome. Before liberation come to presence years and years back, the electric power enterprises have been overwhelmed by utilities that had full command over movements of every kind nearby. In any case, after its first endeavor in Latin America, the business has been on the move in many nations all over the planet. In a liberated market, end-use clients have the decision to choose their power provider. Keywords: Load Forecasting, ,Load forecasting models, Electricity pricing forecasting, AI Based Load forecasting, Neural network model

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Tsao, Yu-Chung, Thuy-Linh Vu i Jye-Chyi Lu. "Electric Power Supply Chain Networks Design featuring Differential Pricing and Preventive Maintenance". RAIRO - Operations Research 55, nr 2 (marzec 2021): 1137–52. http://dx.doi.org/10.1051/ro/2021044.

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The electric power supply chain network plays an important role in the world economy. It powers our homes, offices, and industries and runs various forms of transportation. This paper considers an electric power supply chain network design problem featuring differential pricing and preventive maintenance. We demonstrate that this general model can be formulated as the centralized and decentralized supply chain models. A continuous approximation approach is used to model the problems. The objective of these models is to determine the optimal power plants’ service area, electricity price, and preventive maintenance budget while maximizing the total network profit or the own organization’s benefits. Our model is applied to the case of a power company in northern Vietnam. We show that the proposed approach can be used to address real-world cases effectively. The results demonstrate that the use of differential pricing policy and preventive maintenance could much enhance power company profit.

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KluÄŤka, Jozef. "RISKS OF REGULATION IN NETWORK INDUSTRY â€“ CASE OF THE SLOVAK REPUBLIC". CBU International Conference Proceedings 5 (22.09.2017): 228–31. http://dx.doi.org/10.12955/cbup.v5.930.

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Historically, the Slovak Republic infrastructure involved monopolies, with the State operating sectors of electricity, telecommunications, postal services, gas, and water supply through state enterprises. The electric energy sector has since become privatized in the Slovak Republic. Because of insufficient competition (oligopoly), a regulatory office formed with the role of price regulation. In this pricing area, the electricity supply sector suffered turmoil in the Slovak Republic. This paper addresses some practicalities of regulation and proposes measures to minimize risks relating to decisions of the regulating authority. The current issues reflect problems of price regulation of electricity in the Slovak Republic that have resulted in political disputes. This paper describes the regulatory framework of network industries in the Slovak Republic and general assumptions for transparent and efficient regulatory process. It includes an analysis of certain aspects of price regulation and the role and responsibilities of a chairman and other stakeholders in this process. Some measures are proposed to minimize the future risks of price regulation. These measures cover internal subjects and processes as well as the external environment involving stakeholders and their influence on negotiating prices.

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Kahawala, Sachin, Daswin De Silva, Seppo Sierla, Damminda Alahakoon, Rashmika Nawaratne, Evgeny Osipov, Andrew Jennings i Valeriy Vyatkin. "Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing". Energies 14, nr 14 (20.07.2021): 4378. http://dx.doi.org/10.3390/en14144378.

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Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed the challenges of multi-step prediction (Predicting multiple time steps into the future) that is accurate, robust and real-time. This paper proposes a novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in Real-time (RIPPR), that overcomes these challenges. RIPPR utilizes Variational Mode Decomposition (VMD) to transform the spot price data stream into sub-series that are optimized for robustness using the Particle Swarm Optimization (PSO) algorithm. These sub-series are inputted to a Random Vector Functional Link neural network algorithm for real-time multi-step prediction. A mirror extension removal of VMD, including continuous and discrete spaces in the PSO, is a further novel contribution that improves the effectiveness of RIPPR. The superiority of the proposed RIPPR is demonstrated using three empirical studies of multi-step price prediction of the Australian electricity market.

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Wang, Ge, Qi Zhang, Hailong Li, Yan Li i Siyuan Chen. "The impact of social network on the adoption of real-time electricity pricing mechanism". Energy Procedia 142 (grudzień 2017): 3154–59. http://dx.doi.org/10.1016/j.egypro.2017.12.383.

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Abdin, Islam, Yan-Fu Li i Enrico Zio. "Risk assessment of power transmission network failures in a uniform pricing electricity market environment". Energy 138 (listopad 2017): 1042–55. http://dx.doi.org/10.1016/j.energy.2017.07.115.

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Pownall, Thomas, Iain Soutar i Catherine Mitchell. "Re-Designing GB’s Electricity Market Design: A Conceptual Framework Which Recognises the Value of Distributed Energy Resources". Energies 14, nr 4 (20.02.2021): 1124. http://dx.doi.org/10.3390/en14041124.

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The design of electricity markets determines the technologies, services and modes of operation that can access value, consequently shaping current and future electricity landscapes. This paper highlights that the efficacy of Great Britain’s electricity market design in facilitating net zero is inadequate and must be reconfigured. The rules of the current electricity market design are remnants of an electricity sector dominated by large-scale, centralised, fossil fuel technologies. Therefore, routes to market for the provision of necessary services to support net zero, not least flexibility, are largely inaccessible for distributed energy resources and, despite their benefits to the system, are thus undervalued. Based upon a review and consolidation of 30 proposed electricity market designs from liberalised electricity sectors, this paper proposes a new electricity market design for Great Britain. This design is presented alongside a new institutional framework to aid in the efficient operation of the market. Specifically, this paper proposes a new local balancing and coordinating market located at each grid supply point (the transmission and distribution interface). This is realised through the implementation of a distributed locational marginal pricing structure which is governed by the evolution of the current distributed network operator, known as the distributed service provider (DSP). The DSP also operates a local balancing and ancillary market for their geographical area. The wholesale market is reconfigured to coordinate with these new local markets and to harmonise the actors across the distribution and transmission network.

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Haider, Sajjad, i Peter Schegner. "Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid". Energies 13, nr 22 (19.11.2020): 6069. http://dx.doi.org/10.3390/en13226069.

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It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms of voltage drops, and line loading by way of optimization of the locations at which EVs can charge, is significant. To investigate this, a heuristic optimization approach is proposed to optimize EV charging locations within one feeder, while minimizing nodal voltage drops, cable loading and overall cable losses. The optimization approach is compared to typical unoptimized results of a monte-carlo analysis. The results show a reduction in peak line loading in a typical benchmark 0.4 kV by up to 10%. Further results show an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for a reduction in transmission losses shows insignificant savings for subsequent simulation. These optimization methods may allow for the introduction of spatial pricing across multiple nodes within a low voltage network, to allow for an electricity price for EVs independent of temporal pricing models already in place, to reflect the individual impact of EVs charging at different nodes across the network.

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Weyman-Jones, Thomas. "Energy Price Decoupling and the Split Market Issue". Energies 16, nr 16 (10.08.2023): 5910. http://dx.doi.org/10.3390/en16165910.

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Load scheduling and dispatch by merit order on electricity generation networks has used a wholesale market electricity system operator model focused on system marginal pricing, in which the spot price of electricity at any point in time is equal to the system marginal cost given by the higher value of the price, which rations demand to capacity or the operating cost of the most expensive plant on the system, which is usually a fossil fuel price. This idea has come under challenge because renewable technologies such as wind power farms or solar power farms are treated as having close to zero operating costs. The challenges, under the general heading of energy price decoupling, include suggestions for operating split markets possibly overseen by a regulator, and the prediction that marginal cost pricing should be abandoned. This review evaluates these in terms of their economic impact, relating them to the policy debates on electricity market reform.

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Ghassemi, Abolfazl, Pejman Goudarzi, Mohammad R. Mirsarraf i T. Aaron Gulliver. "A Stochastic Approach to Energy Cost Minimization in Smart-Grid-Enabled Data Center Network". Journal of Computer Networks and Communications 2019 (20.03.2019): 1–11. http://dx.doi.org/10.1155/2019/4390917.

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We propose a Lyapunov drift-plus-penalty- (LDPP-) based algorithm to optimize the average power cost for a data center network. In particular, we develop an algorithm to minimize the operational cost using real-time electricity pricing with the integration of green energy resources from the smart grid. The LDPP technique can achieve significant energy cost savings under quality of service (QoS) constraints. Numerical results are presented to evaluate and validate our solution. These results illustrate significant operational/energy cost reductions for a data center network over the conventional approach which optimizes the predicted values of stochastic parameters under a fixed QoS constraint.

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Kawano, Yu, Eiji Sawada, Takahiro Yasui i Toshiyuki Ohtsuka. "Spot Pricing of Electricity Market with Network Structure Composed of Consumers, Suppliers, and Transmission Companies". Transactions of the Institute of Systems, Control and Information Engineers 30, nr 9 (2017): 366–72. http://dx.doi.org/10.5687/iscie.30.366.

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Chaves-Ávila, José Pablo, Reinier A. C. van der Veen i Rudi A. Hakvoort. "The interplay between imbalance pricing mechanisms and network congestions – Analysis of the German electricity market". Utilities Policy 28 (marzec 2014): 52–61. http://dx.doi.org/10.1016/j.jup.2013.11.005.

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Moti, Md Mahraj Murshalin Al, Rafsan Shartaj Uddin, Md Abdul Hai, Tanzim Bin Saleh, Md Golam Rabiul Alam, Mohammad Mehedi Hassan i Md Rafiul Hassan. "Blockchain Based Smart-Grid Stackelberg Model for Electricity Trading and Price Forecasting Using Reinforcement Learning". Applied Sciences 12, nr 10 (19.05.2022): 5144. http://dx.doi.org/10.3390/app12105144.

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A smart grid is an intelligent electricity network that allows efficient electricity distribution from the source to consumers through telecommunication technology. The legacy smart grid follows the centralized oligopoly marketplace for electricity trading. This research proposes a blockchain-based electricity marketplace for the smart grid environment to introduce a decentralized ledger in the electricity market for enabling trust and traceability among the stakeholders. The electricity prices in the smart grid are dynamic in nature. Therefore, price forecasting in smart grids has paramount importance for the service providers to ensure service level agreement and also to maximize profit. This research introduced a Stackelberg model-based dynamic retail price forecasting of electricity in a smart grid. The Stackelberg model considered two-stage pricing between electricity producers to retailers and retailers to customers. To enable adaptive and dynamic price forecasting, reinforcement learning is used. Reinforcement learning provides an optimal price forecasting strategy through the online learning process. The use of blockchain will connect the service providers and consumers in a more secure transaction environment. It will help tackle the centralized system’s vulnerability by performing transactions through customers’ smart contracts. Thus, the integration of blockchain will not only make the smart grid system more secure, but also price forecasting with reinforcement learning will make it more optimized and scalable.

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Zhou, FenghuaZou, Wu i Gu. "Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing". Energies 12, nr 13 (4.07.2019): 2587. http://dx.doi.org/10.3390/en12132587.

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This paper presents a detailed description of data predictive control (DPC) applied to a demand-side energy management system. Different from traditional model-based predictive control (MPC) algorithms, this approach introduces two model-free algorithms of artificial neural network (ANN) and random forest (RF) to make control strategy predictions on system operation, while avoiding the huge cost and effort associated with learning a grey/white box model of the physical system. The operating characteristics of electrical appliances, system energy consumption, and users’ comfort zones are also considered in the selected energy management system based on a real-time electricity pricing system. Case studies consisting of two scenarios (0% and 15% electricity price fluctuation) are delivered to demonstrate the effectiveness of the proposed approach. Simulation results demonstrate that the DPC controller based on ANN pays only 0.18% additional bill cost to maintain users’ comfort zones and system economy standardization while using only 0.096% optimization time cost compared with the MPC controller.

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Hernández Rodríguez, Marcos, Luis Gonzaga Baca Ruiz, David Criado Ramón i María del Carmen Pegalajar Jiménez. "Artificial Intelligence-Based Prediction of Spanish Energy Pricing and Its Impact on Electric Consumption". Machine Learning and Knowledge Extraction 5, nr 2 (2.05.2023): 431–47. http://dx.doi.org/10.3390/make5020026.

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The energy supply sector faces significant challenges, such as the ongoing COVID-19 pandemic and the ongoing conflict in Ukraine, which affect the stability and efficiency of the energy system. In this study, we highlight the importance of electricity pricing and the need for accurate models to estimate electricity consumption and prices, with a focus on Spain. Using hourly data, we implemented various machine learning models, including linear regression, random forest, XGBoost, LSTM, and GRU, to forecast electricity consumption and prices. Our findings have important policy implications. Firstly, our study demonstrates the potential of using advanced analytics to enhance the accuracy of electricity price and consumption forecasts, helping policymakers anticipate changes in energy demand and supply and ensure grid stability. Secondly, we emphasize the importance of having access to high-quality data for electricity demand and price modeling. Finally, we provide insights into the strengths and weaknesses of different machine learning algorithms for electricity price and consumption modeling. Our results show that the LSTM and GRU artificial neural networks are the best models for price and consumption modeling with no significant difference.

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Pablo Luna, Juan, Claudia Sagastizábal i Paulo J. S. Silva. "A discussion on electricity prices, or the two sides of the coin". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, nr 2202 (7.06.2021): 20190428. http://dx.doi.org/10.1098/rsta.2019.0428.

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We examine how different pricing frameworks deal with non-convex features typical of day-ahead energy prices when the power system is hydro-dominated, like in Brazil. For the system operator, requirements of minimum generation translate into feasibility issues that are fundamental to carry the generated power through the network. When utilities are remunerated at a price depending on Lagrange multipliers computed for a system with fixed commitment, the corresponding values sometimes fail to capture a signal that recovers costs. Keeping in mind recent discussions for the Brazilian power system, we analyse mechanisms that provide a compromise between the needs of the generators and those of the system operator. After characterizing when a price supports a generation plan, we explain in simple terms dual prices and related concepts, such as minimal uplifts and bi-dual problems. We present a new pricing mechanism that guarantees cost recovery to all agents, without over-compensations. Instead of using Lagrange multipliers, the price is defined as the solution to an optimization problem. The behaviour of the new rule is compared to two other proposals in the literature on illustrative examples, including a small, yet representative, hydro-thermal system. This article is part of the theme issue ‘The mathematics of energy systems’.

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Nakabi, Taha Abdelhalim, i Pekka Toivanen. "Optimal price-based control of heterogeneous thermostatically controlled loads under uncertainty using LSTM networks and genetic algorithms". F1000Research 8 (10.09.2019): 1619. http://dx.doi.org/10.12688/f1000research.20421.1.

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In this paper, we consider the problem of thermostatically controlled load (TCL) control through dynamic electricity prices, under partial observability of the environment and uncertainty of the control response. The problem is formulated as a Markov decision process where an agent must find a near-optimal pricing scheme using partial observations of the state and action. We propose a long-short-term memory (LSTM) network to learn the individual behaviors of TCL units. We use the aggregated information to predict the response of the TCL cluster to a pricing policy. We use this prediction model in a genetic algorithm to find the best prices in terms of profit maximization in an energy arbitrage operation. The simulation results show that the proposed method offers a profit equal to 96% of the theoretical optimal solution.

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Alizadeh, Mahnoosh, Hoi-To Wai, Andrea Goldsmith i Anna Scaglione. "Retail and Wholesale Electricity Pricing Considering Electric Vehicle Mobility". IEEE Transactions on Control of Network Systems 6, nr 1 (marzec 2019): 249–60. http://dx.doi.org/10.1109/tcns.2018.2809960.

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Alsaid, Loai Ali, i Jean Claude Mutiganda. "Accounting and smart cities: New evidence for governmentality and politics". Corporate Ownership and Control 17, nr 3 (2020): 158–70. http://dx.doi.org/10.22495/cocv17i3art12.

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The concept of a smart city has attracted the attention of many scholars and policymakers in many countries worldwide. The role of accounting as a tool of governance in smart city politics, however, has so far been largely overlooked, especially in less developed countries (LDCs). This paper sets off to fill this research gap and hitherto unexplored linkages between accounting and smart cities. Drawing on the concept of governmentality, the authors conducted a case study based on document analysis, meetings observation, and 42 semi-structured interviews at a branch of a hybrid electricity company owned by New Cairo City in Egypt, during 2018. Findings show that the case company has implemented smart distribution networks of electricity in which new management accounting technology (enterprise resource planning (ERP) system) is used to trace costs, revenues, client complaints and feedback in a timely manner. The new network (of infrastructure and technologies) has represented timely accounting information as a major political power to influence accurate governance decision-making, such as smart electricity pricing and control, and to challenge governance decisions that are not sound. This paper is one of the first studies to explore the socio-political dynamics of accounting in smart city governance in the context of LDCs.

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Maanavi, Masoud, Arsalan Najafi, Radu Godina, Mehrdad Mahmoudian i Eduardo M. G. Rodrigues. "Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing". Applied Sciences 9, nr 14 (15.07.2019): 2817. http://dx.doi.org/10.3390/app9142817.

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The energy management of virtual power plants faces some fundamental challenges that make it complicated compared to conventional power plants, such as uncertainty in production, consumption, energy price, and availability of network components. Continuous monitoring and scaling of network gain status, using smart grids provides valuable instantaneous information about network conditions such as production, consumption, power lines, and network availability. Therefore, by creating a bidirectional communication between the energy management system and the grid users such as producers or energy applicants, it will afford a suitable platform to develop more efficient vector of the virtual power plant. The paper is treated with optimal sizing of DG units and the price of their electricity sales to achieve security issues and other technical considerations in the system. The ultimate goal in this study to determine the active demand power required to increase system loading capability and to withstand disturbances. The effect of different types of DG units in simulations is considered and then the efficiency of each equipment such as converters, wind turbines, electrolyzers, etc., is achieved to minimize the total operation cost and losses, improve voltage profiles, and address other security issues and reliability. The simulations are done in three cases and compared with HOMER software to validate the ability of proposed model.

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KAWASAKI, Koki, Tomohiro TAKATA, Yoshiro FUKUI i Tadahiro TANIGUCHI. "Risk-Limiting Real-Time Pricing for a Regional Prosumers' Electricity Network with Distributed Solar Power Generation". SICE Journal of Control, Measurement, and System Integration 10, nr 2 (2017): 100–109. http://dx.doi.org/10.9746/jcmsi.10.100.

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