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With the gradual increase in the penetration rate of renewable energy, the multifunctional role of pumped storage is becoming increasingly prominent, and the joint operation of "renewable energy + pumped storage" is a current research hotspot. However, during the joint system operation, there are dual risks from internal (renewable energy output) and external (market prices) factors, which significantly impact the system's overall revenue. Therefore, an analysis is conducted around the operational mechanism of the "wind power–pumped storage" joint operation, and the uncertain factors faced during the system's operation are identified. Second, an optimization model for wind power–pumped storage under deterministic scenarios is constructed, employing robust optimization theory and information gap decision theory to describe the uncertainty of electricity prices and wind power, thus forming a hybrid of the information gap decision theory and the robust optimization model for wind power–pumped storage. Finally, the results show that: (1) The total revenue of the model proposed in the paper has increased by 2.36% compared to the robust optimization model and by 9.04% compared to the deterministic model, significantly enhancing the model's robustness and risk resistance capabilities. (2) From the perspective of the economic feasibility of different energy storage system configurations, the wind plant equipped with pumped storage has the highest economic feasibility, with an internal rate of return of 9.8% and net present value of 872 million Chinese Yuan, which is higher than that of compressed air energy storage and electrochemical energy storage systems. (3) Decision makers can set the risk deviation coefficient and the uncertainty budget according to their risk preferences, thereby changing the robustness of the model for differentiated decision making. However, an increase in the uncertainty budget coefficient will cause the total revenue of the joint operation system first to increase and then decrease, with the maximum revenue achievable within the range of 500–625; the total revenue reaches its maximum when the risk deviation coefficient is between 0.1 and 0.125. [ABSTRACT FROM AUTHOR]

Introduction: This paper constructs a revenue model for an independent electrochemical energy storage (EES) power station with the aim of analyzing its full life-cycle economic benefits under the electricity spot market. Methods: The model integrates the marginal degradation cost (MDC), energy arbitrage, ancillary services, and annual operation and maintenance (O&M) costs to calculate the net profits of the EES power station. Using an iterative optimization approach, we determine the optimal MDC and analyze the economic end of life (EOL) for different types of EES power stations. Results: By examining real-world examples from the California energy market, we find that the full life-cycle benefits of an EES power station peak when its MDC is optimal, at $45/MWh-throughput. Under these conditions, the economic and physical EOL of commercial/industrial EES power station is 9 years, while the economic EOL of residential-grade EES power station is 8 years, which is shorter than their physical EOL of 9 years. Discussion: The study further indicates that the economic life of an EES power station is influenced by multiple factors, and operators need to determine the optimal economic EOL to maximize revenue based on battery degradation characteristics, market conditions and operational strategy. [ABSTRACT FROM AUTHOR]

IntroductionThis paper constructs a revenue model for an independent electrochemical energy storage (EES) power station with the aim of analyzing its full life-cycle economic benefits under the electricity spot market.MethodsThe model integrates the marginal degradation cost (MDC), energy arbitrage, ancillary services, and annual operation and maintenance (O&M) costs to calculate the net profits of the EES power station. Using an iterative optimization approach, we determine the optimal MDC and analyze the economic end of life (EOL) for different types of EES power stations.ResultsBy examining real-world examples from the California energy market, we find that the full life-cycle benefits of an EES power station peak when its MDC is optimal, at $45/MWh-throughput. Under these conditions, the economic and physical EOL of commercial/industrial EES power station is 9 years, while the economic EOL of residential-grade EES power station is 8 years, which is shorter than their physical EOL of 9 years.DiscussionThe study further indicates that the economic life of an EES power station is influenced by multiple factors, and operators need to determine the optimal economic EOL to maximize revenue based on battery degradation characteristics, market conditions and operational strategy.

Micro coal-fired units (MCFU) and combined heat and power plants (CHP) in integrated energy system (IES) will emit a large amount of carbon dioxide when providing loads to customers, which will lead to higher operating costs of IES. To solve this challenge, an optimal dispatch model of power-to-gas (P2G) and methane reactor (MR) considering the reward and punishment costs based on carbon emission trading mechanism is proposed, to reduce carbon emissions of IES and enhance the accommodation of renewable energy (RE). Subsequently, considering the uncertainty of RE, a combination optimization method for MCFU and CHP units is developed based on the Lagrange multiplier method. Finally, considering the mechanism of electricity spot market (ESM), a transaction strategy for IES participating in ESM is proposed to further enhance the accommodation of RE. The effectiveness of the proposed framework is demonstrated through simulations. [ABSTRACT FROM AUTHOR]

As the proportion of new energy sources, such as wind power, in the electricity system rapidly increases, their participation in spot market competition has become an inevitable trend. However, the uncertainty of clearing price and wind power output will lead to bidding deviation and bring revenue risks. In response to this, a bidding strategy is proposed for wind farms to participate in the spot market jointly with carbon capture power plants (CCPP) that have flexible regulation capabilities. First, a two-stage decision model is constructed in the day-ahead market and real-time balancing market. Under the joint bidding mode, CCPP can help alleviate wind power output deviations, thereby reducing real-time imbalanced power settlement. On this basis, a tiered carbon trading mechanism is introduced to optimize day-ahead bidding, aiming at maximizing revenue in both the electricity spot market and carbon trading market. Secondly, conditional value at risk (CVaR) is introduced to quantitatively assess the risks posed by uncertainties in the two-stage decision model, and the risk aversion coefficient is used to represent the decision-maker's risk preference, providing corresponding strategies. The model is transformed into a mixed-integer linear programming model using piecewise linearization and McCormick enveloping. Finally, the effectiveness of the proposed model and methods is verified through numerical examples. [ABSTRACT FROM AUTHOR]

The electricity system faces load-matching pressure due to imbalances during peak times or extreme weather, making it costly to phase out coal-fired electricity and reduce carbon emissions. The electricity spot market pilot (ESMP) policy provides reserve capacities for grids, but its effectiveness on green economic development is still unknown. Therefore, using panel data from 286 cities in China from 2006 to 2019, this study analyzes the impact of the ESMP policy on green economic performance. The results indicate a significantly positive effect of the ESMP policy on green economic performance. In addition, the policy could help to phase out 6.3% of coal-fired capacity, on average, reduce coal-fired equipment utilization hours by 0.9%, and increase renewable equipment utilization hours by 4% in pilot regions. These findings provide new insights into ESMP policy and green economic development. [ABSTRACT FROM AUTHOR]

On March 3, 2022, the National Development and Reform Commission of China and the National Energy Administration of China jointly published the Notice on Accelerating the Construction of the Electricity Spot Market. The notice indicated that the government would support the uninterrupted operation of the power spot pilot project with conditions and form a long-term and stable electricity spot market as soon as possible. These steps will help rapidly improve China's electricity spot market construction. Due to its large-scale energy storage capacity, the Concentrated Solar Power (CSP) station can be used as a peak-regulating power station to provide grid-friendly long-term peak regulating capacity and moment of inertia for the power system. When CSP stations participate in electricity spot market trading, they can optimize their operation strategy to generate more electricity during periods with higher electricity prices, thereby obtaining more profits. In this paper, a 100MW CSP Peak-Regulating Station with an 80.0hm2 reflecting area of Heliostats Field (HF) is designed in Gansu, China. In the Peak-Regulating operation strategy, the most suitable TES Capacity for this project is determined to be 15h. We take this CSP station as the research objective to optimize the operation strategy of the power station. The research results indicate that optimizing the operation strategy will increase the power station's annual electricity sales revenue by about 19.97% before the strategy was optimized when the power station participated in the real-time trading of the Gansu electricity spot market in 2022.

As the reform of electricity marketization advances, the virtual power plant (VPP), as an emerging market participant, is being progressively incorporated into the trading scope of both the electricity spot market (ESM) and the reserve market (RM). Due to its flexible regulation capabilities, the bidding strategies of VPPs in the electricity market are complex and variable, presenting new challenges to the joint operation of power markets. In this context, first, based on the interactive game relationships among multiple market entities under the operating mechanisms of the spot and reserve markets, this paper designs a principal-follower game framework for transactions in these markets with the VPP as the main entity. Second, it constructs a two-level joint optimized bidding strategy model for VPP participation in the spot and reserve markets, where the inner layer is the VPP’s optimized bidding strategy model aiming to maximize the total revenue from the spot and reserve markets; the outer layer is the spot and reserve market clearing model targeting the minimization of the total electricity purchasing cost for society. Finally, the simulation case analysis shows that the proposed method can achieve joint optimal operation of the electricity spot and reserve markets by considering the VPP bidding strategy, resulting in a 1.42% reduction in the total electricity purchase cost of the power market.

In the electricity spot market (ESM), thermal power plants (TPP) are quoted based on fuel costs and carbon costs, which impose carbon costs from the generation side to the load-side. The cost of load-side participation in the ESM will increase further. In addition, the traditional ESM centralized bidding model is based on the marginal clearing price, without considering the line transmission constraints, which may affect the security of the power system. To solve this challenge, firstly, an ESM centralized bidding clearing model including the generation side and the load-side is established based on the transaction mechanism of centralized bidding in the ESM. In this model, the marginal clearing prices of two possible bidding scenarios in the ESM are considered. Secondly, the marginal clearing price of the ESM is designed based on the security constraint of the power flow, which can avoid power transmission exceeding its capacity. Finally, a carbon emission cost settlement mechanism is proposed based on the carbon emission flow (CEF) method, which can reduce the carbon emission cost paid by the load-side. The effectiveness of the method is verified by simulations. Compared with a traditional load-side settlement model, the settlement cost on the load-side is reduced by 30.45 %.

Alongside China’s new round of electricity reform, Yunnan has implemented medium- and long-term market reform in the electricity sector since 2015 with little progress seen in establishing an electricity spot market. To this end, this paper attempts to identify the pathway to an electricity spot market for Yunnan through its realities and lessons learned from Europe. As such, on the ground of the status quo of the energy mix, generation features, load structure and profiles, power supply–demand situations, and market progress in the Yunnan electricity system, this paper reviews two classical market frameworks commonly adopted worldwide, from the perspectives of market organizing and the core optimization model broadly used in coordinating system operation and clearing the market. Then the contrast of Yunnan with the European experience is analyzed with respect to the energy mix, generation, transmission, and exchange, as well as the political and economic institutional arrangements, market conditions, and driving forces. Moreover, the Yunnan case study scenarios and likely consequences and benefits under different market frameworks are further analyzed to verify the edge of a centralized optimal-power-flow-based regional market over a decentralized cross-province balancing market, in productively reducing price distortion, allocating resources, maintaining power supply–demand balance at all times, and promoting renewable energy integration across a wider region. Finally, policy suggestions concerning the pathway to an electricity spot market are provided for Yunnan in further deepening its electricity reform.

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Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The electricity spot market plays a significant role in promoting the self-improvement of the overall resource utilization efficiency of the power system and advancing energy conservation and emission reduction. This paper analyzes and compares the potential impacts of spot market operations on system planning, considering the differences between planning methods in traditional and spot market environments through theoretical analysis and model comparison. Furthermore, we conduct research and analysis on grid planning methods under the spot market environment with the goal of maximizing social benefits. Unlike the pricing approach based on historical price data in traditional market simulation processes, a data-driven approach that combines experimental economics and machine learning is proposed, specifically using mixed empirical learning to simulate unit bidding strategies in market transactions. A simulation model for electricity spot market trading is constructed to analyze the performance of the planning results in the spot market environment. The case study results indicate that the proposed planning methods can enable the grid to operate well in the spot market environment, maintain relatively stable nodal prices, and ensure the integration of a high proportion of clean energy. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As China has declared its pledge on peak emission and carbon neutrality and the target of building a new power system， the new energy in China is undergoing a period of rapid development. However， since the subsidies for new energy continues to decrease， it is widely recognized that new energy consumption and the revenues should be addressed through a market-oriented way. The paper simulates and estimates new energy participating in market trade by “quoted volume without quotation” based on the trading mechanism of electricity spot market in Zhejiang province. The results show that new energy， through rational safeguarding of utilization hours and rational utilization hours consumption， can ensure its own revenues and in the meantime reduce subsidies by participation in market； in contrast to wind power， solar PV is more receptive to electricity spot market.

With the advancement of electricity market reform, eight pilot spot provinces in China have started electricity spot market trading. In the settlement rules of spot markets in each region, some areas apply nodal weighted average price to the settlement’s customer and supply sides. The economic incentive effect of the spot market pricing mechanism on market participants affects their bidding strategies. Therefore, based on the current research on electric spot market pricing mechanisms, this paper investigates the effects of bidding procedures and fairness of generator and consumption sides under three settlement mechanisms: locational marginal pricing, zonal pricing, and average system pricing. Through multi-agent-based electricity market simulation, the paper establishes a spot market clearing and settlement model to quantitatively analyze the impact of zonal settlement mechanisms on the interests of each market player. By validating analysis results, the paper makes suggestions for selecting electricity price mechanisms in China’s electric spot market environment.

Multi-interval settlement is conducive to meeting the needs of growing renewable energy resources with great intermittency and volatility and managing the effective operation in the electricity spot market. However, the insufficient incentive of market price inaccurately reflecting the total cost of the electricity spot market caused by the inaccuracy of generation and load prediction in the current multi-interval settlement will lead to inefficient market scheduling, causing the market participants to deviate from dispatch instructions. Based on the problem above, a new multi-interval settlement system of rolling-horizon scheduling including the period selection of look-ahead schedules and enhanced settlement mechanism is proposed to improve the price incentive for the electricity spot market. The proposed multi-interval settlement system can produce a better look-ahead period and a more economically efficient dispatch solution inducing dispatch-following incentives. A numerical example shows that the proposed multi-interval settlement system outperforms the traditional settlement mechanism regarding economic efficiency.

The development of renewable energy in China has made remarkable achievements, but the problem of renewable energy consumption has become increasingly prominent. This paper establishes a power market trading system for renewable energy, with the aim of promoting large-scale renewable energy consumption and increasing the enthusiasm of renewable energy producers and users to participate in market transactions. First, according to the power generation cost, the backup cost of renewable energy power plants and the possible quotation strategies of other renewable energy producers, a quotation model of renewable energy producers is established. In the clearing of the spot market by renewable energy producers, the independent market operator conducts the first-stage clearing of the electricity market with the goal of maximizing social welfare. After the announcement of the clearing results, the renewable energy producers that did not win the bid will revise their quotations and carry out the second stage clearing to realize the consumption of renewable energy. In this paper, the particle swarm algorithm combined with the CPLEX solver is used to solve the problem, and finally, different scenarios are analyzed through example analysis. The results show that, compared with the conventional power market trading mechanism, the energy abandonment rate of the power market trading mechanism for renewable energy proposed in this paper drops from 8.2% to 2.1%, and the profit margin of renewable energy producers increase by 6.6%. It is demonstrated that the proposed electricity market mechanism can effectively promote the consumption of renewable energy and increase the income of renewable energy producers. [ABSTRACT FROM AUTHOR]

With the increasing development of China's electricity spot market, electricity retailers attract power users to participate in power demand response by designing electricity plans so as to improve the interests of both parties. In this paper, the coefficient of peak avoiding response is introduced. The incentives of electricity plans are formulated on the basis of time-of-use electricity price to attract power users to actively transfer their load. Firstly, according to the designed electricity plans, the load transmission is calculated after the power users choose the electricity plans. Secondly, considering the influence of the change of electricity expenditure and the change of electricity consumption mode on the user utility, a decision model of electricity plans based on the utility function is established to calculate the selection of electricity plans. Thirdly, four kinds of electricity plans with the goal of maximizing the profit drive of electricity retailers are designed based on the load transmission of electricity retailers participating in spot market transactions. Finally, it is verified that the optimized electricity plans meet the interests of both parties by the analysis of an example.

It is urgent to vigorously develop renewable energy to achieve the goal of “carbon peaking and carbon neutrality”. Unlike traditional thermal units, the marginal cost of renewable energy units is zero. With the reform of the electricity spot market, renewable energy is bound to have a huge impact on the market-clearing results and the operation of the power system. Considering the actual spot market operating rules, an electricity spot market simulation framework is established based on the security-constrained unit commitment and economic dispatch models for the operation simulation of the electricity spot market. Taking the actual data of a provincial power grid as an example, a quantitative analysis of the impact of renewable energy on the market-clearing results in the spot market environment is conducted. The simulation results show that, in the spot market environment, the participation of renewable energy will reduce the average electricity price, the system operating costs, and the renewable energy accommodation. At the same time, the profit space of the market units will be compressed.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the intermittent and anti-peak shaving characteristics of the new energy generator sets, the phenomenon of power abandonment hinders direct participation in the electricity market transactions. The hybrid electricity market can use spot market transactions to absorb renewable energy to a large extent. The multi-energy complementary operation coupling of the hybrid electricity market transactions can exploit the complementation and substitution between different energy sources, realize flexible energy production, consumption, storage, and transmission, and optimize the allocation of resources on a larger scale. In this paper, a mid-long-term spot transaction coordination scheduling (MTCS) model for a multi-energy system is constructed by considering the medium- and long-term electricity market uncertainty and the trial operation characteristics of the spot power market in China. A two-stage solution method is introduced to solve the complex multi-agent, multi-period, and multi-energy model. The results of testing this model on the Gansu region, one of the first eight spot pilot areas in China, are presented and discussed in detail. The results showed that this MTCS model could reduce the opening of thermal power units to a more considerable extent, prioritize the consumption of new energy power generation, and reduce the output uncertainty of new energy through the hybrid power market. [ABSTRACT FROM AUTHOR]

The electricity market is evolving rapidly from pre-bid to spot markets, where the transactions between producers and consumers have become crucial and are vulnerable to safety risks. In addition, the new modes of transaction are also becoming popular and are said to have some risks. To reveal the impact of the new electricity transaction mode—spot market—on the safety risk and generation benefit, this article presents a simulation method connecting the transaction results and operation process. To verify the effectiveness of the proposed method, an actual cascade hydropower station (CHS) in the Dadu River basin, Southwestern China, which comprises eight hydropower stations, is selected. Hydraulic power matching among the CHSs is discriminated from four aspects with multiple indicators combined with the reality of a hydropower-dominated market in Sichuan, China. The dispatching decision-making process of hydropower generators is described with a multi-objective optimization model and then solved with a fast search strategy based on the classical output calculation method in hydrology. The security risks and economic damage hidden in the day-ahead market on a certain day are discovered and analyzed. The technology proposed provides support for bidding decisions in spot transactions and satisfies the reality during the transition period of electricity reform.

The energy transition, a process in which fossil fuels are being replaced by cleaner sources of energy, comes with many challenges. The intrinsic uncertainty associated with renewable energy sources has led to a search for complementary technologies to tackle those issues. In recent years, the use of electric vehicles (EVs) has been studied as an alternative for storage, leading to a much more complex market structure. Small participants are now willing to provide energy, helping to keep the desired balance of supply and demand. In this paper, we analyse the electricity spot market, providing a model where EVs decide to participate depending on the underlying conditions. We study pricing rules adapted from versions currently in use in electricity markets, and focus on two of them for our experimental settings: integer programming (IP) and extended locational marginal (ELM) pricing. We particularly pay attention to the properties those prices might satisfy, and numerically test them under some scenarios representing different levels of participation of EVs and an active demand side. Our results suggest that IP pricing generally derives larger individual uplift payments and further produces public prices that are not well aligned with the final payments of market participants, leading to distortions in the market. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of poor control accuracy and long time consuming in traditional deviation electric quantity control methods, a real-time deviation electric quantity control model design for regional electricity spot market is proposed. Through the base electric quantity, generation side electric quantity and consumption side electric quantity, the electricity market assessment and settlement results are obtained. The Monte Carlo method is used to simulate the generation of user-side load scenarios and the corresponding scenario probability, set electric quantity assessment and settlement constraints, verify the actual total electric quantity consumption of all users in the current month, the actual total interactive electric quantity and the actual total exchange market electricity, and obtain the actual total base electric quantity of the current month. Calculate the difference between the actual electric quantity and the planned electric quantity, determine the allowable range of real-time control deviation electric quantity, and use the rolling compensation method to control the electric quantity consumption side. The experimental results show that the proposed model has an error of only 1% for real-time deviation electric quantity control in the regional electricity spot market, and the control time is short. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Day-ahead prediction of electricity market price is a key for market participants to make a bidding strategy. While numerous methods for day-ahead market price (DMP) forecasting have been developed and applied, those lack interpretable models for DMP forecasting. This study provides a new hybrid model for DMP forecasting, centered on eXtreme Gradient Boosting (XGBoost). The suggested proposal uses neural networks (NNs) with entity embedding (EE) to preprocess categorical feature data and applying Shapley Additive exPlanations (SHAP) to select optimal features. In addition, the weights of NNs and SHAP are interpretability methods. The embedding weights from NNs can reveal the distribution of categorical features in the multi-dimensional space by t-distributed Stochastic Neighbor Embedding (t-SNE). SHAP can explain the impact of input features on the forecast result by assessing the importance of global and local features. The proposed model has been proven to be high accuracy, interpretability and outperforms other models on the test set accuracy, including support vector machine, artificial neural works, classification and regression trees, and random forest). Furthermore, the study found that those load/capacity ratios, the historical clearing prices, penetration of renewable energy, month, hour, weekdays, and weekends have a substantial impact on the prediction of the DMP. [ABSTRACT FROM AUTHOR]

The effective identification of the economic withholding behavior of the generators can help ensure the fair operation of the electricity market. A SCAD-logit model is proposed to improve the performance of the logit model for the massive data of electricity market. First, a social network analysis method is used to construct an equity relationship graph of the generators to obtain a set of key monitoring generators. An indicator system for identifying the economic withholding behavior of the generators is constructed based on structure conduct performance (SCP) theory. The indicators are screened by the smoothed clipped absolute deviation (SCAD) penalty regression method to reduce the collinearity and improve identification efficiency. Then, a SCAD-logit model is established to identify the economic withholding of key monitoring generators, so that the boundary contributions of each indicator to the economic withholding behavior are obtained. The confusion matrix, ROC curve, and AUC values are used to evaluate the model's performance. Finally, the model is applied to the electricity spot market, and the method can identify the generators that exercise economic withholding behavior with a correct rate of 96.83%. Indicators such as market share, quotation fluctuation degree, high quotation index, and volume price index can be used as important indicators for identifying the economic withholding behavior. [ABSTRACT FROM AUTHOR]

Under the China’s ‘dual carbon’ national goal–reaching peak carbon emissions by 2030 and achieving carbon neutrality by 2060, one of the key issues in China is how to smoothly transit from a fixed-price mode to a competitive market pricing mode for renewable energy generation companies. Aiming at minimizing governmental subsidies and maximizing the fairness among renewable energy generation companies, a multi-agent three-layer transition mechanism with the transactions of green certificates considered is proposed in this paper. Through adjusting subsidy policies, the developed transition mechanism can stimulate the renewable energy generation companies to gradually participate in the competitive electricity spot market. Specifically, a multi-market multi-agent transaction framework in the transition mechanism is first established. Then, in order to derive the important parameters of the transition mechanism, a method that decouples the electricity market and the green certificate market is designed. Finally, the feasibility and efficiency of the proposed transition mechanism are demonstrated through numerical examples.

In the rapid promotion of China's electricity spot market, a large number of electricity retailers and large consumers participate in power trading, of which medium- and long-term power trading accounts for a large proportion. In the electricity spot market, the previous medium- and long-term transactions need to be closely combined with the current spot market transaction settlement rules. This paper analyzes the trading strategy of large retailers in the power market. In order to effectively reduce the total electricity cost, it is necessary to optimize the medium- and long-term transactions based on three aspects: electricity quantity and benchmark price decisions of medium- and long-term contracts, the daily electricity decomposition method in the day-ahead (DA) market, and the daily load curve decomposition strategy. According to load history characteristics that are extracted by the X12 method, daily electricity is decomposed from the medium- and long-term electricity quantity in the DA market. This paper introduces three methods of decomposing the daily load curve and proves that the particle swarm algorithm is the best method for effectively minimizing the cost in the DA market. Through analyzing the total electricity cost change pattern, we prove that the basic component of decision making is the relative relationship between the electricity price of medium- and long-term contracts and the equivalent kWh price of medium- and long-term electricity in the DA market, which is determined by the decomposition daily curve method. If the equivalent kilowatt-hour price obtained by the decomposition method in the DA market is greater than the electricity price of medium- and long-term contracts, the larger the electrical energy of medium- and long-term contracts, the lower the costs. Based on the above principles, electricity retailers can carry out planning for medium- and long-term transactions, as well as the decomposition and declaration of the daily electricity quantities and daily load curves. [ABSTRACT FROM AUTHOR]

Demand response (DR) program empowers the dynamic prices to actively optimize the consumption. This optimized consumption plays a vital role in resolving the complex operation and reliability issues in the electricity market. The human behavior aspect of consumers explained by several models that have been reported in the literature. These models depend on the classical utility factor. The effect of price on the consumer's decision in the field of energy efficiency and reduction of consumption based on behavioral characteristics are two important aspects of DR programs. In absence of such characteristics, results become non‐viable. In this paper, the footprint of two time‐based DR programs is explored on the peak reduction namely; Real‐Time Pricing (RTP) and Peak Time Rebate (PTR). Artificial Neural Network (ANN) based topologies for two DR programs are proposed. The proposed topologies employ variation in demand and price, subsequently for simulating an online DR simulator. Demand before and after the RTP and PTR was calculated and compared with four ANN‐based DR topologies namely; Radial Basis Function Neural Network‐Demand Response (RBFN‐DR), Feedforward Backprop‐Demand Response (FFBP‐DR), Layer Recurrent‐Demand Response (LR‐DR), and Generalized Regression‐Demand Response (GR‐DR). The proposed models are tested on three test cases. The first case tested on hourly data of New England ISO of Connecticut on August 18, 2014, the second case tested on hourly data of the same system on August 18, 2020, and the third case tested on hourly residential data of test smart grid. By assessing the results from all three test cases, depicted that RBFN‐DR proved its efficacy by giving better results for both price‐based programs namely; RTP and PTR. [ABSTRACT FROM AUTHOR]

Summary: The transition from fossil fuels to renewable energies such as wind and solar is being encouraged worldwide. Renewable energy investment costs have been dropping, which, combined with growing incentives, has led to a rapid rise in renewable capacity. These changes in the electricity market entail great challenges and uncertainties, specifically due to resource availability. Wind and solar energy depend on seasonal weather conditions to generate energy, such as wind and radiation, respectively. However, few studies have focused on analyzing and measuring the impact of a progressive increase of renewable energy in a short‐term electricity market and its effect on energy security. We developed a System Dynamics model to be able to analyze alternative scenarios and levels of uncertainties. We studied the impact of different shares of renewable sources on economic dispatch and energy security through reliability, reserve margin, resilience, and vulnerability measures. High shares of renewable resources (>60%) impact the energy security—reliability (< 98%), reserve margin (<−2%), resilience (>4%), and vulnerability—and may lower the spot price due to zero variable costs, which reduce the profitability of conventional firms in the short term and give closure to firms in the middle term and long term. Novelty Statement: In this study, a system dynamics model is proposed to assess the electricity market configuration and the spot price while renewable energy sources (wind and solar) increase and conventional sources fall (hydropower and fossil fuel). Energy security is measured by including energy measures such as reliability, resilience, vulnerability, and reserve margin in a day‐ahead market. The penetration of renewable energy creates a horizontal movement of the supply curve to the right, affecting wholesale electricity price, energy security, and firms' profitability. Highlights: The effect of different shares of renewable energy in a day‐ahead electricity market was studied.Energy security under renewable energy share on economic dispatch was considered.The analyses were performed using a system dynamics model for a theoretical market.High shares of renewable resources may lower the spot price due to zero variable costs.Low spot price reduces the profitability of both conventional and nonconventional firms. [ABSTRACT FROM AUTHOR]

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The energy transition, a process in which fossil fuels are being replaced by cleaner sources of energy, comes with many challenges. The intrinsic uncertainty associated with renewable energy sources has led to a search for complementary technologies to tackle those issues. In recent years, the use of electric vehicles (EVs) has been studied as an alternative for storage, leading to a much more complex market structure. Small participants are now willing to provide energy, helping to keep the desired balance of supply and demand. In this paper, we analyse the electricity spot market, providing a model where EVs decide to participate depending on the underlying conditions. We study pricing rules adapted from versions currently in use in electricity markets, and focus on two of them for our experimental settings: integer programming (IP) and extended locational marginal (ELM) pricing. We particularly pay attention to the properties those prices might satisfy, and numerically test them under some scenarios representing different levels of participation of EVs and an active demand side. Our results suggest that IP pricing generally derives larger individual uplift payments and further produces public prices that are not well aligned with the final payments of market participants, leading to distortions in the market.

The reference price is the most important signal for market participant to have position of selling or buying in the trade of electricity. The electricity price has a dynamic structure and is directly and/or indirectly affected by many factors. All of the market participants' transaction is carried out in a contract based on forecasting. These transactions are for day-ahead market (DAM) and balancing power market 1 day before, intraday market 60 min before and short, mid, long term for derivatives market. The price is forecasted that is determined as far as cost. The price forecasting has benefits which are maximizing profit, protecting from all of the crisis and constrictions and mitigation of loses. All of the market participants highly affected from the price. For this reason, such an important topic should be conducted scientifically. This study aims to develop a price forecasting tool for short term which is calculated day by day in DAM at Turkish electricity market. This forecasting tool is developed based on artificial neural networks. All of the market participants take the price, which is calculated in DAM, as reference. There are many direct and/or indirect factors affecting the reference price used by market players. In this study, a short-term price forecasting model was formed by analyzing historical data and all of the data, which are affecting the price most correctly, are correlated by using artificial neural network method. The basis of the forecasting model used is supply and demand curves. All the factors affecting different points on supply and demand curves by artificial neural network model provide the most correct reference price. In the study, January, April, July and October 2017 values were taken and estimated for randomly selected days of the months. The randomly selected supply and demand curves of April 19 is affected from renewable energy production, natural gas and coal production. In this scope of conclusion, the forecasted price is below 5% deviation on average per day compared to the actual price. As for hourly error deviation, it was seen that deviation is below 10% for 13 h. The suggested artificial neural network model scientifically supports market participant to make short-term transaction. [ABSTRACT FROM AUTHOR]

This study presents an approach of biogas generation coupled with electricity trading under electricity spot market to maximise the biogas power profits. The aim of this study is to achieve a minimal generation cost for the biogas generation system while eliminating the dependence on batteries due to the storage and tunable properties of biogas digester. In terms of the electricity prices at different times in the spot market, the operating control strategy for biogas generation is established to perform the optimal generation scheduling, which give the two model comparison results. The practical case of biogas engineering in the ecological park in Hunan Province in China is carried out to validate the effectiveness and practicability of the proposed strategy.