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Globally, the power sector must undergo a profound transition to achieve the decarbonization development targets. Various roadmaps are implemented, but only from a macro perspective, lacking the consideration of the electricity market rules. In this paper, we develop and present a market-driven generation and transmission expansion planning (MGTEP) model considering the effectiveness of the electricity market. Specifically, generation and transmission companies incorporate hourly market trading and annual capacity investment into strategic decisions to maximize their profits, with the supply function equilibrium model to analyze bidding behaviors. An equivalent quadratic programming formulation is deployed to solve the trilevel MGTEP model. Meanwhile, the MGTEP model is coupled with decarbonization policies to support the state and federal government in assessing energy transition strategies. We implement the MGTEP model with carbon emission allowance and carbon tax policies for the southern China electricity market to achieve carbon peaking by 2030. Carbon emission allowance adopts an intensity-based cap based on generation companies' historical output. The case study results show that 50 % carbon emission allowance or 400 CNY/t carbon tax is required but with several drawbacks, including unsatisfactory decarbonization effect, excessive economic sacrifice, etc. Finally, the case study is extended to dual-track policies with different combinations of policies. An optimal combination is 70 % carbon emission allowance and 160 CNY/t carbon tax. In this case, the power sector's carbon dioxide emissions and electricity prices in the southern China electricity market would increase to 554.6 Mt and 864.34 CNY/MWh in 2030, respectively, along with a carbon price of 850 CNY/t.

To maximize the expected profits and manage the risks of renewable energy system under electricity market environment, scenario-based- stochastic optimization model can be established to generate energy bidding strategies, in which the probabilistic scenarios of risk parameters are usually obtained by using statistical or machine learning methods. This paper proposes a practical multivariate statistical method for risk parameter scenario generation, which is used by a wind energy system faced with uncertain electricity prices and wind power productions, and it considers the correlation between dependent risk parameters by using historical data directly. The probabilities of scenarios containing correlated risk parameters are calculated by using multivariate histograms, in which the asymmetric correlation between different parameters existing in the historical data are preserved. Additionally, in order to make the stochastic optimization problem with large numbers of scenarios tractable, a multivariate scenario reduction method is used to trim down the scenario number. By solving the stochastic optimization problem, optimal day-ahead bidding curves for the wind energy system are generated, and Douglas-Peucker algorithm is used to fit the bidding curves according to market requirements. Case studies based on real world data in electricity markets are performed to prove the effectiveness of the proposed risk parameter scenario generation method and energy bidding strategies. Finally, conclusions and practical suggestions on future research works are provided. [ABSTRACT FROM AUTHOR]

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It is challenging for the European power system to exactly predict RES output and match energy production with demand due to changes in wind and sun intensity and the unavoidable disruptions caused by severe weather conditions. Therefore, in order to address the so-called "flexibility challenge" and implement the variable RES production, the European Union needs flexible solutions. In order to accommodate quicker reactions, compared to those performed today, and the adaptive exploitation of flexibility, grid operators must adjust their operational business model, as the electrical grid transitions from a fully centralized to a largely decentralized system. OneNet aspires to complete this crucial step by setting up a new generation of grid services that can fully utilize distributed generation, storage, and demand responses while also guaranteeing fair, open, and transparent conditions for the consumer. Using AI methods and a cloud-computing approach, the current work anticipates that active management of the power system for TSO–DSO coordination will be improved by the web-based client-server application F-channel. In the current work, a user's experience with the platform for a Business Use Case (BUC) under the scenario of severe weather conditions is presented. The current work aims to increase the reliability of outage and maintenance plans for the system operators (SOs) by granting them a more accurate insight into the conditions under which the system may be forced to operate in the upcoming period and the challenges that it might face based on those conditions. In this way, the methodology applied in this case could, via AI-driven data exchange and analyses, help SOs change the maintenance and outage plans so the potential grave consequences for the system can be avoided. The SOs will have accurate forecasts of the relevant weather parameters at their disposal that will be used in order to achieve the set targets. The main results of the presented work are that it has a major contribution to the optimal allocation of the available resources, ensures the voltage and frequency stability of the system, and provides an early warning for hazardous power system regimes. [ABSTRACT FROM AUTHOR]

To promote the use of renewable energy (RE), several types of RE policies have been implemented globally. Among these, feed-in tariff (FiT) is one of the most accepted mechanisms of pricing policy. However, choosing the best policy for a market with a high RE penetration is a challenge. A case of Bhutan is considered in this paper as the source of electrical energy is predominantly hydropower. Additionally, a generous subsidy is provided by the government to keep the electricity tariff to a minimum. Recently, there has been an increase in the advocacy and motivation for other forms of RE sources (RES) to supplement hydropower and increase energy security in Bhutan. Bhutan aims to achieve a total of 20 MW of non-hydro RES by 2025 as per the RE policy of Bhutan. However, Bhutan still does not have an RE pricing policy, and therefore, there is a need to institute a suitable pricing mechanism to accommodate the penetration of the planned non-hydro RES. This paper discusses the challenges in introducing FiT for non-hydro RES in an electricity market dominated by hydropower in Bhutan. Subsequently, recommendations are made in the wake of subsidised electricity tariff, which is the lowest in the region at 0.0171 USD/kWh for low voltage customers. FiTs for solar photovoltaic based on different categories of customers have been computed and proposed. [ABSTRACT FROM AUTHOR]

To facilitate wind energy use and avoid low returns, or even losses in extreme cases, this paper proposes an integrated risk measurement and control approach to jointly manage multiple statistical properties of the expected profit distribution for a wind storage system. First, a risk-averse stochastic decision-making framework and multi-type risk measurements, including the conditional value at Risk (CVaR), value at risk (VaR) and shortfall probability (SP), are described in detail. To satisfy the various needs of multi-type risk-averse decision makers, integrated risk measurement and control approaches are then proposed by jointly considering the expected, boundary and probability values of the extreme results. These are managed using CVaR, VaR and SP, respectively. Finally, the effectiveness of the proposed risk control strategy is verified by conducting case studies with realistic market data, and the results of different risk control strategies are analyzed in depth. The impacts of the risk parameters of the decision maker, the energy capacity of the battery storage and the price difference between the day-ahead and real-time markets on the expected profits and risks are investigated in detail. [ABSTRACT FROM AUTHOR]

The synthesis of methane from hydrogen and carbon dioxide creates an energy resource that is suitable for long-term storage. Once this process is powered by renewable electricity, it produces a clean fuel for producing electricity and heat and supports large-scale renewable energy deployment, energy transition and climate change mitigation. This paper proposes a pragmatic approach to assessing the economic potential of synthetic methane-based power. Today, natural gas plays an important role in the Baltic region due to the existing infrastructure, which includes a transmission and distribution pipeline network, gas power plants and a large underground storage reservoir. Replacing natural gas with synthetic methane would fulfil carbon emission reduction ambitions. In this paper, we simulate electricity producers' actions at market conditions and consider the generation portfolio in the Baltics and the interconnections with Scandinavia and Poland operating in the NORDPOOL electricity market. As a result of these calculations, we obtain the volume of the synthetic gas, the production costs, the volume of gas storage, the installed capacity of the gas power plant, and the investments required to ensure energy transition and system adequacy. These results are essential for the informed decisions made by policymakers, investors and system operators. [ABSTRACT FROM AUTHOR]

In order to accelerate the construction of a clean, low-carbon, safe and efficient energy system, China set the provincial weight of responsibility for renewable energy power consumption and established a renewable energy power absorption guarantee mechanism in 2019. As a market incentive policy, it has enduring effect on the low-carbon transformation of the power industry. Firstly, the operation mechanism of the renewable energy consumption guarantee mechanism is analyzed. The general framework, core elements and supporting measures are clarified. Secondly, a stock-flow diagram is constructed based on the system dynamics method. It contains the green electricity market sub-module, the green-certificate market sub-module and the excess power absorption market sub-module. Finally, multiple scenarios are set up to simulate the impact of the green-certificate market and excess power absorption market improvements on the installed capacity and tariff of China's green power market. The renewable energy guarantee mechanism is an effective means to promote the consumption of green electricity in China. In addition, in the short term the cost of electricity for users has increased, but in the long term the cost of electricity shows a fluctuating downward trend. This study provides theoretical references for the formulation of clean and low-carbon policy objectives in the power industry and the optimization of market mechanisms. [ABSTRACT FROM AUTHOR]

We deal with several sources of uncertainty in electricity markets. The independent system operator (ISO) maximizes the social welfare using chance constraints to hedge against discrepancies between the estimated and real electricity demand. We find an explicit solution to the ISO problem and use it to tackle the problem of a producer. In our model, production, as well as the income of a producer, are determined based on the estimated electricity demand predicted by the ISO, which is unknown to producers. Thus, each producer is hedging against the uncertainty of the prediction of the demand using the value-at-risk approach. To illustrate our results, a numerical study of a producer's best response given a historical distribution of both estimated and real electricity demand is provided. [ABSTRACT FROM AUTHOR]

Predicting the price gap between the day-ahead Market (DAM) and the real-time Market (RTM) plays a vital role in the convergence bidding mechanism of Independent System Operators (ISOs) in wholesale electricity markets. This paper presents a model to predict the values of the price gap between the DAM and RTM using statistical machine learning algorithms and deep neural networks. In this paper, we seek to answer these questions: What will be the impact of predicting the DAM and RTM price gap directly on the prediction performance of learning methods? How can exogenous weather data affect the price gap prediction? In this paper, several exogenous features are collected, and the impacts of these features are examined to capture the best relations between the features and the target variable. An ensemble learning algorithm, namely the Random Forest (RF), is used to select the most important features. A Long Short-Term Memory (LSTM) network is used to capture long-term dependencies in predicting direct gap values between the markets stated. Moreover, the advantages of directly predicting the gap price rather than subtracting the price predictions of the DAM and RTM are shown. The presented results are based on the California Independent System Operator (CAISO)'s electricity market data for two years. The results show that direct gap prediction using exogenous weather features decreases the error of learning methods by 46 % . Therefore, the presented method mitigates the prediction error of the price gap between the DAM and RTM. Thus, the convergence bidders can increase their profit, and the ISOs can tune their mechanism accordingly. [ABSTRACT FROM AUTHOR]

Copyright of Southern Energy Construction is the property of Southern Energy Construction 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.)

Electricity forecasting is very important for the electricity generation and distribution markets from the consumer's point of view. Over the last fifteen years, there has been a significant change in the patterns toward deregulation and restructuring of the power industry. Competition in the wholesale and retail markets together gives open access to the transmission network and attracts customers with many benefits. In this paper, ERCOT electricity market modeling, price analysis, and forecasting the demand based on the market participation using machine and deep learning techniques is considered to advance the profit among the Generating Companies (GenCos) for real smart single-ended bidding in the wholesale electricity market. For efficient market involvement to accommodate the congestion cost, the Locational Marginal Pricing (LMP) method is considered. The impact of the microgrid as a non-dispatchable load with the GenCos and Load Serving Entity (LSE) on an Eight-bus ERCOT system is developed and proposed intelligent bidding approach for effective GenCos participation in meeting the social welfare accommodating the optimal location of Microgrid using AMES python tool to enhance the market operation effectively. [ABSTRACT FROM AUTHOR]

Mathematical models are given for forming the schedule of electric energy supply from hydroelectric power stations for bidding in the "day-ahead" market segment. The formulation of the task of forming the supply schedule as a task of optimal use of available hydro resources for the production of electrical energy at hydroelectric power stations is described. The need to perform two-stage optimization based on price and non-price criteria is substantiated. It is proposed to consider non-price criteria by means of penalty functions in the objective function of optimization by price criteria. A system of basic technological limitations, as well as equations for control and adjustment of the reservoir filling level, is given. The results of practical calculations for both optimization options are presented. References 6, figures 3. [ABSTRACT FROM AUTHOR]

This work focused on prescribing, designing, implementing, and evaluating a pilot project conducted in the Greek power system that addressed balancing and congestion management issues that system operators (SOs) face within the clean energy era. The considered pilot project fully focused on the development of the F-channel platform, including the idea behind this application, the steps that were taken in the process, and the outcomes of the performed activities fitting into the overall picture of the OneNet project. The specified F-channel platform is a web-based, client-server application that uses artificial intelligence (AI) techniques and cloud computation engines to improve the management of the active power for the TSO-DSO coordination. The flexibility of the grid's resources was identified, and an integrated monitoring system based on the precise forecasting of variable generation and demand was implemented. The focus areas were congestion management, frequency control, and voltage control services, for which corresponding network models were created in close cooperation with system operators. The obtained results are essential for the remaining demonstration results because they offer an incredibly accurate basis for further research into their use in congestion management and other weather-related enhanced transmission and distribution system planning and operation practices. [ABSTRACT FROM AUTHOR]

With the growing popularity of charging pile infrastructure and the development of smart electronic devices and 5G communication technologies, the electric vehicle aggregator (EVA) as a bidding entity can aggregate numerous electric vehicle (EV) resources to participate in the electricity market. Moreover, as the number of grid-connected EVs increases, EVA will have an impact on the nodal marginal prices of electricity market clearing. Aiming at the bidding and offering problem of EVA participation in the day-ahead and intra-day electricity markets, based on the Stackelberg game theory, this paper establishes a bilevel optimization model for EVA participation in the two-stage electricity market as a price-maker. In the proposed bilevel model, the upper-level and lower-level models are constructed as an operational problem for EVA and a market-clearing problem for independent system operator (ISO), respectively. In the day-ahead stage, EVA is optimized to maximize its own expected benefits, and ISO aims to improve the social benefits. In the intra-day stage, EVA is optimized to maximize its self-interest, and the ISO aims to make it possible to minimize the cost of expenditures to maintain the system's supply–demand balance. Karush–Kuhn–Tucker (KKT) conditions and dual theory are used to transform the nonlinear bilevel programming model into a mixed-integer single-level linear programming model. In order to verify the validity of the proposed bilevel model as well as to comparatively analyze the impact of EVA's participation in the electricity market on the market clearing results. Two scenarios are set up where EVA is seen as the price-taker in Scenario 1 and EVA is seen as the price-maker in Scenario 2. ISO's revenue under Scenario 2 increased by USD 2262.66 compared to Scenario 1. In addition, the EVA acts as an energy consumer in Scenario 1 with a charging cost of USD 26,432.95, whereas in Scenario 2, the EVA can profit by participating in the electricity market with a revenue of USD 26,432.95, at which point the EVA acts like a virtual power plant. The simulation examples verify that the proposed bilevel optimization model can improve the benefits of ISO and EVA at the same time, achieving mutual benefits for both parties. In addition, the simulation analyzes the impact of abandonment penalty price on ISO and EVA intra-day revenues. Comparing the scenarios where the abandonment penalty price is 0 with USD 10/MW, the ISO's revenue in the intra-day market decreases by USD 197.5. Correspondingly, EVA's reserve capacity is dispatched to consume wind power in the intra-day market, and its revenue increases by USD 197.5. The proposed two-stage bilevel optimization model can provide a reference for EVA to develop scheduling strategies in the day-ahead and intra-day electricity markets. [ABSTRACT FROM AUTHOR]

Georgia signed an Association Agreement with the European Union in 2014, and this launched a process of approximation and harmonization with EU law. Energy law is one of the most important areas, which has to be developed and modified in accordance with the EU directives, regulations and rules of the Energy Community. Georgia took responsibility for establishing the energy exchange system and reorganizing the Georgian electricity market on a new model. In fact, these issues have not been studied, as they require, on the one hand, a very in-depth, practical knowledge of the issue and, on the other hand, erudition in the issues of legal approximation and information about the obligations assumed by the association agreement. The purpose of the article is to review the legislative regulations on the Georgian electricity market, the legal framework that defines the main principles of the market, the basis of operation, and sociopolitical and legal mechanisms of market stability. In the article, special attention will be paid to the status of the energy exchange in Georgia, its concept, its legal basis, problematic issues related to its implementation, and the future perspective. As a result of the analysis of the issues, based on the evaluation of the existing problems, the necessary legal ways of the development of energy law and the mechanisms promoting harmonization with the laws of the European Union are determined. [ABSTRACT FROM AUTHOR]

The current Chinese electricity market and carbon market are built relatively independently, without coupling and synergy, and the incoherence between the two markets is beginning to emerge. Carbon emissions costs in the carbon trading market will affect the marginal cost of renewable energy and thermal power in the electricity market, limiting market participants' profitability. In order to simulate and evaluate the changes of LCOE of renewable energy and thermal power in the "electricity-carbon" market environment, this paper presents the calculation method of carbon emission cost of thermal power and CCER benefit of renewable energy based on the relevant regulations in China, and calculates the carbon emission cost transmission rate of thermal power based on Cournot model. In addition, we proposed a method for calculating the LCOE based on the international common calculation method for LCOE, combined with China's taxation policy and the cost and benefit factors of renewable energy and thermal power in the carbon market, and proposed a method for calculating the LCOE applicable to the "electricity-carbon" market environment in China. The findings indicate that as a result of the influence of the carbon market, the levelized cost of energy (LCOE) cost of thermal power will increase, and the profitability of thermal power in the electricity market will be further reduced. On the other hand, the LCOE cost of renewable energy will further decrease, and its profitability will improve due to the additional CCER benefits in the carbon market. [ABSTRACT FROM AUTHOR]

In the context of the joint operation of China's intra-provincial markets and inter-provincial trading, how to meet the load demand and energy consumption using inter-provincial renewable energy trading is a key problem. The combined operation of intra-provincial and inter-provincial markets provides a new way for provincial power companies to optimize and clear the intra-provincial power market, complete the intra-provincial consumption responsibility weight index, and consume renewable energy across provinces and regions. This paper combines power generation and consumption within the province, uses inter-provincial renewable energy trading to meet the load demand within the province and completes the index of intra-provincial consumption responsibility weights. The intra-provincial market trading and inter-provincial market clearing are respectively taken as the upper and lower levels of the model. Under the two-level electricity market operation framework, the upper-level model aims to minimize the expected total operating cost within the province considering the carbon emission cost and the weight of the consumption responsibility, while the lower-level model aims to minimize the inter-provincial renewable energy purchasing cost. Finally, the influence of inter-provincial transaction mechanism, risk aversion coefficient, voucher price, and responsibility weight on operating cost is analyzed. Simulation is used to verify that the proposed model can meet the requirements of the provincial load power consumption and the consumption responsibility weight index, and promote the consumption of renewable energy. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology 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.)

Copyright of Power Generation Technology is the property of Power Generation Technology 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.)

Copyright of Power Generation Technology is the property of Power Generation Technology 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.)

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity volatility based on existing HAR models. The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon, whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily, weekly, and monthly horizons. The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility, and in most cases, dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects. The out-of-sample results were robust across three different methods. More importantly, we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.

With the continuous improvement of energy sector marketization in China， carbon market and electricity market have become critical policy instruments for controlling carbon emissions and regulating electricity supply and demand respectively. However， the two markets have different policies， management and trading systems， their operations are independent of each other， and the market prices have not been connected. In this context， the policy background and development status of China’s carbon market， electricity market， and green power trading are first summarized. Then， the principle and key issues of the coordinated operation of the carbon market and electricity market are analyzed. Finally， the construction of a coordinated operation system between the carbon market and the electricity market is discussed in light of the actual market operation to facilitate their coordinated operation and jointly promote the low-carbon development of the energy sector.

A mathematical model of the daily optimization of available hydro resources of hydropower plants is presented as a tool for solving the problem of determining hourly volumes of electricity sales in the «day ahead» market segment, taking into account technological limitations and volumes of electricity under already concluded agreements. The method of preparation of input data in the part of calculations of the coefficients of the objective function and the system of constraints is presented. An example of solving the problem of daily optimization of the available hydro resources of the Kyiv HPP is demonstrated and the need to use additional non-price optimization criteria is substantiated. Bibl. 7, figure.

[Introduction] To achieve the "carbon peak and neutrality" goals, the ways for enterprise energy conservation and emission reduction need to fully explored, and the green and low-carbon policy tools should be fully exploited. Green hydrogen, as a clean energy, has become the key to social low-carbon transition thanks to its full link zero carbon emission. Carbon emission rights trading market is a key policy tool to control and reduce carbon emissions by making use of the market-oriented mechanism. Building a carbon trading center hydrogen energy industry trading mechanism to achieve the coupling of carbon trading and hydrogen energy will greatly promote the achieving of the "carbon peak and neutrality" goals. [Method] Based on this, a coupling mechanism of "green hydrogen market - national carbon trading market - electricity market" was built in this paper. The "green hydrogen market - national carbon trading market - electricity market" was modeled and simulated by further utilizing the system dynamics method, and the interaction of multiple markets was studied. [Result] The results show that: Firstly, the coupling among the green hydrogen market, carbon trading market and electricity market can be realized through green hydrogen certification and carbon quota trading, and it is found that the coupling model is feasible through simulation. Secondly, it is found through the simulation of the basic scenario that the coupling of multiple markets can stimulate the increase of carbon price and control the increase of thermal power generation and green hydrogen production. Finally, increasing the proportion of green hydrogen certification and improving the obsolete unit elimination mechanism and carbon quota auction mechanism will help form a carbon pricing mechanism. [Conclusion] On the one hand, this study diversifies the green hydrogen trading models, promotes the participation of green hydrogen in carbon trading to obtain certain benefits, and reduces the hydrogen energy cost. On the other hand, it makes use of the multi-market linkage mechanism to bring into play the forced effect of carbon price and promote the reduction of the proportion of traditional fossil hydrogen production and thermal power generation.

This study analyzes the feed-in tariff strategy of steel enterprises in the context of the steel industry’s electricity market, which is composed of waste heat and pressure (WHP) power generation and traditional thermal power generation. A Stackelberg game model was developed to compare three pricing strategies (i.e., fixed price, fixed premium, and variable premium) for the feed-in tariff of WHP power generation technology among steel enterprises, traditional fossil fuel power generators, and local governments. Three pricing strategies for WHP generation feed-in tariffs are compared, and numerical simulations are run to examine the effects of market size, WHP generation cost coefficients, and steel firm environmental costs on optimal regulated prices, optimal steel firm profits, and optimal total social welfare, respectively. The following findings are obtained. (1) For optimal price regulation, increasing the market size and the coefficient of WHP generation cost has little effect on the optimal price regulation under the fixed price policy. The only way to increase the price of the fixed price policy is to increase the environmental cost of steel enterprises. Moreover, increasing the market size and the coefficient of WHP generation cost will reduce the price of fixed premia. Additionally, increasing the market size and the coefficient of WHP generation cost will reduce the price of fixed premia. Increases in market size and WHP generation costs will raise the price of variable premium insurance, whereas increases in enterprise environmental costs will lower and maintain the price of variable premium policy. (2) For optimal steel enterprise profit, increasing the market size and the cost coefficient of WHP generation will increase the profit of steel enterprises under the fixed premium policy. However, it will have little effect on the profit of the fixed price and variable premium policies. Moreover, increasing the environmental cost of enterprises will reduce the profit of the fixed premium policy, but it will have little effect on the profit of the fixed price and variable premium policy. (3) For optimal total social welfare, increasing the market size and environmental cost of steel enterprises can increase total social welfare under the fixed price policy. Moreover, increasing the coefficient of WHP generation cost has little effect on the fixed price policy welfare; increasing both the market size and the coefficient of WHP generation cost has little effect on the fixed premium policy welfare. Additionally, increasing the environmental cost of enterprises can increase the fixed premium policy welfare, and increasing the market size can increase the total variable premium policy welfare. Meanwhile, increasing the coefficient of WHP generation cost and the environmental cost of steel enterprises can reduce the variable premium policy welfare and finally level off. (4) Depending on the decision maker’s preferences, various optimal decisions can be made. Higher subsidies imply higher optimal regulation prices, which are accompanied by market riskiness, thus influencing the rate of market development of waste heat and waste pressure power feed-in tariffs. In optimal rule prices, low riskiness and low subsidies are fixed price strategies, high riskiness and moderate subsidies are fixed premium strategies, and high riskiness and high subsidies are variable premium strategies. In optimal steel firm profit, low riskiness and low subsidy, high riskiness and high subsidy, and low riskiness and high subsidy are fixed price, fixed premium, and variable premium strategies, respectively. In optimal total social welfare, moderate riskiness and moderate subsidy, low riskiness and low subsidy, and high riskiness and high subsidy are fixed price, fixed premium, and variable premium strategies, respectively.

Abstract Cloud energy storage (CES) in the power systems is a novel idea for the consumers to get rid of the expensive distributed energy storages (DESs) and to move to using a cloud service centre as a virtual capacity. Although the different characteristics and applications of the energy storages are reviewed in some papers, there is no review study on the CES concepts, formulations, applications, and challenges. Therefore, the main contribution of this paper is to review the applications of the CES and its technical challenges in the power systems. For this purpose, the concept and fundamentals of the CES, as well as their role in supporting the consumers and the power network, are described first. The flow of information in a CES is then discussed, and the roles of the operator, consumers, and facilities, as the main sectors of the CES are explained. The existing studies are classified and discussed regarding the different applications of the CES in the power systems and their drawbacks are highlighted. The operation and planning (feasibility) problems of the CES are investigated. Reviewing the existing studies shows that comprehensive models are required to address the energy management (EM) and feasibility analysis of the CES applications. To address this challenge, the general formulations are presented for the planning and the operation scheduling problems of the CES. In addition, addressing different CES applications in the power systems leads to some technical challenges which are described. Finally, future directions are suggested for potential researchers to continue the studies on the CES integration and application.

The stochastic output of wind power will lead to the penalty of bidding deviation in the spot market and bring bidding risk, restricting the participation of wind power in market competition. The combined bidding strategy for multiple power generation stakeholders can help alleviate the output uncertainty of wind power, reducing the bidding deviation penalty. This paper aims at the modeling method for wind-thermal combined bidding in the spot power market. Conditional value at risk (CVaR) is used to describe the bidding risk of the combined bidding. To better reflect the bidding risk, the bilateral tail information of the upper and lower deviations is modeled according to the revenue function. The uncertainties of wind power output and the clearing price in the day-ahead market are considered, and the risk decision-making model for wind-thermal combined bidding is established. The model is solved by the chaotic particle swarm optimization algorithm with constraint handling (EDFC-PSO). The results show that the proposed modeling method can describe the risk indicators in detail, providing a better reference for market bidding. Also, the combination of the wind farm and the thermal power unit can reduce the risk of wind power in bidding and improve its profitability in the day-ahead market. Besides, the risk speculation of the wind farm can be found when facing the inevitable increased risks. It means the behaviors of the market participants can be better explained using the proposed method in this paper.

The European Union, in pursuit of the goal of reducing emissions by at least 55% by 2030 and achieving climate neutrality by 2050, is deploying different actions, with industry decarbonization as a key strategy. However, increasing electricity demand requires an intensification of energy generation from clean technologies, and the energy system’s expansion is hindered by renewable generation’s climatic dependencies and the imperative for substantial electrical infrastructure investments. Although the transmission grid is expected to grow, flexibility mechanisms and innovative technologies need to be applied to avoid an overwhelming growth. In this context, this paper presents a thorough assessment, conducted within the FLEXINDUSTRIES project, of the flexibility potential across seven energy-intensive industries (automotive industry, biofuel production, polymer manufacturing, steel manufacturing, paper mills, pharmaceutical industry, and cement production). The methodology followed during the analysis entails reviewing the state-of-the-art existing flexibility mechanisms, industries’ energy markets engagement, and technical/operational readiness. The results highlight the feasibility of the proposed actions for enabling energy market flexibility through demand-response programs, quantifying energy opportunities, and pinpointing regulatory and technical barriers.

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China’s power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side.

Forecasting electricity spot prices holds paramount significance for informed decision-making among energy market stakeholders. This study introduces a methodology utilizing a multilayer perceptron (MLP) neural network for multivariate electricity spot price prediction. The model underwent a feature selection process to identify the most influential predictors. In the validation phase, the model’s performance was evaluated using key metrics, including trend accuracy percentage index (TAPI), normalized root mean squared error (NRMSE), and mean absolute percentage error (MAPE). The results were obtained for a four-week forecast horizon in order to serve as an auxiliary tool to facilitate decision-making processes in the short-term energy market. The relevance of short-term electricity spot price forecasting lies in its direct impact on pricing strategies during energy contract negotiations, which allows for the making of assertive decisions in the energy trading landscape.

Georgia signed an Association Agreement with the European Union in 2014, and this launched a process of approximation and harmonization with EU law. Energy law is one of the most important areas, which has to be developed and modified in accordance with the EU directives, regulations and rules of the Energy Community. Georgia took responsibility for establishing the energy exchange system and reorganizing the Georgian electricity market on a new model. In fact, these issues have not been studied, as they require, on the one hand, a very in-depth, practical knowledge of the issue and, on the other hand, erudition in the issues of legal approximation and information about the obligations assumed by the association agreement. The purpose of the article is to review the legislative regulations on the Georgian electricity market, the legal framework that defines the main principles of the market, the basis of operation, and sociopolitical and legal mechanisms of market stability. In the article, special attention will be paid to the status of the energy exchange in Georgia, its concept, its legal basis, problematic issues related to its implementation, and the future perspective. As a result of the analysis of the issues, based on the evaluation of the existing problems, the necessary legal ways of the development of energy law and the mechanisms promoting harmonization with the laws of the European Union are determined.

Nowadays renewable energy sources, such as wind and solar, whether independently or integrated with other resources, are considered in power system, specifically self-scheduling, bidding and offering strategy problems. However, the uncertain nature of these sources has turned out the greatest challenge for their owners, which makes the bidding and offering in the restructured electricity market more complicated because wind energy generation may cause penalty fees for its generation mismatches. Hence, the primary objective of this paper is to suggest a novel bidding strategy framework based on fuzzy random variable for a wind-thermal system in the electricity market for the first time. The uncertainties associated with day ahead energy, spinning reserve market prices and imbalance prices, are characterized by random fuzzy variables and the uncertainties associated with wind power outputs are modeled as a LR fuzzy numbers. The proposed self-scheduling model maximizes the expected profits while it controls the risk by providing different possibility and probability levels for decision makers.A mathematical modeling approach is applied in this research by using a mixed-integer non-linear programming model which is implemented in Lingo software in a case study of thermal generation unit to investigate the efficiency of the proposed model. A sensitivity analysis is applied to validate the performance of the proposed model. Numerical results reveal that taking advantage of wind power generation alongside with thermal generation will substantially increase the profitability of the integrated generation company.

In deregulated electricity markets, predicting price and load is a common practice. However, market participants and shareholders often seek deeper insights into other system statuses associated with price prediction, such as power flow and market share of generation companies (GenCos). These insights are challenging to obtain using purely data-driven methods. This paper proposes a physics-based solution for the probabilistic prediction of market-clearing outcomes, using real sanitized offer data from the National Electricity Market of Singapore (NEMS). Our approach begins with approximating the generator offers that have been historically cleared. Using this pool of offer data, we propose a probabilistic market-clearing process. This process allows for the probabilistic prediction of market prices. By considering the power system network and its constraints, we also naturally obtain probabilistic predictions of power flow and market shares. We validate our approach using actual NEMS data. Our findings show that while the overall performance of price prediction is comparable to existing methods, our proposed method can also provide probabilistic predictions of other associated system operating conditions. Furthermore, our method enables scenario studies, such as the impact of demand-side participation and the penetration of rooftop photovoltaic (PV) systems on the Uniform Singapore Energy Price (USEP).

The growing popularity of electric vehicles presents a significant challenge to current electric grids since the rising number of these vehicles places additional strain on power systems inside distribution networks. A proposed paradigm is presented for electric vehicles (EVs), which is subsequently partitioned into three distinct dispatching areas to assess its practicality. This study is structured around two primary objectives. The first objective focuses on EV owners aiming to minimize their electricity consumption costs while also receiving compensation for providing services. The second objective involves using an aggregator to establish distinct tariffs for each dispatching area. Additionally, the aggregator aims to shift the charging load demand from peak to off-peak hours and distribute the charging demand to each agent. The authors of this study propose the utilization of a charging and discharging coordination method, specifically the Multi-Agents Charging and Discharging (MACD) algorithm, as a means to successfully tackle the issue of charging demand during peak hours. The objective of the proposed algorithm is to effectively handle the increased charging requirements during peak periods by using vehicle-to-grid (V2G) technologies within the context of Smart Grid systems and electric vehicle (EV) batteries. Importantly, this reduction is achieved without compromising the performance of electric vehicles (EVs) or the convenience experienced by EV owners. The algorithm under consideration demonstrates reduced power charging costs for various sectors. Specifically, it achieves a decrease of 15% for households, 14.6% for corporate buildings, and 14.5% for Industrial Park EV aggregators.

With the development of smart grids, optimizing the performance of response programs in the distribution system in operation and development, held special importance. This paper proposes an optimized method for demand response management in smart grids using real-time pricing. The method involves defining an acceptable consumption pattern and energy price through consumer-utility interaction. For more evaluation, six scenarios are defined that considered different states of energy pricing with and without the Plug-In Electric Vehicles (PHEVs) The first step establishes the demand response with real-time pricing, determining hourly load and energy prices. Price elasticity of demand is employed to gauge consumer load response to energy prices. The second step utilizes the Geometric Mean Optimizer (GMO) to optimize Demand Response Programs (DRPs) for consumers with smart programmable meters. The proposed method is tested on the IEEE-69 bus distribution network under various scenarios, demonstrating its effectiveness under 48 h. Additionally, the study investigates the impact of PHEVs and their controlled charging using the integrated three-tariff method and real-time electricity pricing. The results of the GMO algorithm are also compared with other methods to validate its advantages. The results indicate enhanced voltage profiles and reduced reactive power consumption during peak hours about 4.5% and 23%, respectively. in addition, the load factor by applying the proposed method is increased by about 18%. Moreover, the proposed method by combining the three-tariff method enables the transformer to supply approximately 60% of EVs without overloading, which is 40% more than that case without the three-tariff method.

Chinese electricity market reform poses huge challenges to hydropower operations and electricity trading. This study proposes a scheduling method coupling priority electricity and day-ahead trading for large hydropower plants. The study focuses on complex factors such as tariff uncertainty, different types of electricity settlement rules, and inter-provincial electricity transmission links. Spot market tariff scenarios are determined through the Latin hypercube and the K-means methods. A performance formulation of priority electricity deviation considering settlement assessment rules is established. A transmission description for different sub-plants and a triangular linear interpolation method based on binary independent branching mode are proposed to solve inter-regional transmission connections and hydraulic coupling in cascaded hydropower plants, respectively. Finally, the Big M method is employed to equivalently transform the complex non-linear problem into a mixed-integer linear programming (MILP) model. The method is verified with the day-ahead operation of four large hydropower plants downstream of the Jinsha River in China as a case study. Settlement assessment rules, inter-regional power transmission, and price uncertainty are analyzed in three different cases. Three conclusions are obtained: 1) the priority electricity performance rate and the price are positively correlated, which is useful to guide hydropower plants to actively participate in the market. 2) Introducing the prediction error of electricity price in the model can help avoid market decision risk and improve the expected return by approximately 1.2%. 3) Considering the settlement penalty rule is helpful for power generation enterprises to improve power allocation and thus seek higher revenue compared to traditional methods without considering it.

As the main body of resource aggregation, Virtual Power Plant (VPP) not only needs to participate in the external energy market but also needs to optimize the management of internal resources. Different from other energy storage, hydrogen energy storage systems can participate in the hydrogen market in addition to assuming the backup supplementary function of electric energy. For the Virtual Power Plant Operator (VPPO), it needs to optimize the scheduling of internal resources and formulate bidding strategies for the electric-hydrogen market based on external market information. In this study, a two-stage model is constructed considering the internal and external interaction mechanism. The first stage model optimizes the operation of renewable energy, flexible load, extraction storage, and hydrogen energy storage system based on the complementary characteristics of internal resources; the second stage model optimizes the bidding strategy to maximize the total revenue of the electricity energy market, auxiliary service market and hydrogen market. Finally, a typical scenario is constructed and the rationality and effectiveness of the strategy are verified. The results show that the hybrid VPP with hydrogen storage has better economic benefits, resource benefits and reliability.

Wind energy has been increasingly adopted to mitigate climate change. However, the variability of wind energy causes wind curtailment, resulting in considerable economic losses for wind farm owners. Wind curtailment can be reduced using battery energy storage systems (BESS) as onsite backup sources. Yet, this auxiliary role may significantly weaken the economic potential of BESS in energy trading. Ideal BESS scheduling should balance onsite wind curtailment reduction and market bidding, but practical implementation is challenging due to coordination complexity and the stochastic nature of energy prices and wind generation. We investigate the joint-market bidding strategy of a co-located wind-battery system in the spot and Regulation Frequency Control Ancillary Service markets. We propose a novel deep reinforcement learning-based approach that decouples the system’s market participation into two related Markov decision processes for each facility, enabling the BESS to absorb onsite wind curtailment while performing joint-market bidding to maximize overall operational revenues. Using realistic wind farm data, we validated the coordinated bidding strategy, with outcomes surpassing the optimization-based benchmark in terms of higher revenue by approximately 25% and more wind curtailment reduction by 2.3 times. Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to participating in each market separately. Simulations also show that using curtailed wind generation as a power source for charging the BESS can lead to additional financial gains. The successful implementation of our algorithm would encourage co-location of generation and storage assets to unlock wider system benefits.

In the electricity business, power evacuation from source to load is a challenging task when there is contingency and agencies have to swirl around for solutions in terms of rescheduling generator dispatch, load slashing, adding of network, etc. The more challenging scenario arises when the independent operator wants to re-dispatch a generator during the contingent situation. So, in this paper, we focus on a new generator rescheduling technique with congestion price and transmission security as an intervention. The significant intention of this paper involves restricting the load thereby dispatching a particular generator or a set of generators with low cost and secure transmission line. In addition to this, the network jamming is unpredictable and does not follow any pattern, but power supply in some zones is disrupted due to hidden reasons. Therefore, a macroscopic or holistic approach is adopted for congestion forecasting through demand schedule, gathering, minimum as well as maximum drawls. Here, two significant factors namely the transmission utilization charges as well as transmission congestion charges for predicting the congestion of a transmission line are evaluated. Finally, the experimental analysis to determine transmission utilization charges, transmission congestion charges, cost function and generation supply as well as demand balance with congestion optimization. [ABSTRACT FROM AUTHOR]

China's electricity market reform has posed a real challenge to the large-scale hydropower system. Taking the world's largest watershed hydropower system, the Three Gorges large hydropower system (TGLHS), as the engineering background, this study analyzes the issues and strategies of dispatching and trading in the electricity market. The analysis indicates that the TGLHS exhibits unique difficulties because of transprovincial and transregional power transmission. Major issues including the multi-dimensional and multi-time-scale nested allocation of hydropower energy, the bidding and performance of cascaded hydropower plants in multiple electricity markets, as well as multiple uncertainties in the runoff; electricity prices in multiple markets are also elaborated upon. The corresponding suggested strategies are proposed to cope with the aforementioned issues: (1) for multi-dimensional and multi-scale nested allocation problems, it is necessary to comprehensively consider monthly market transactions and priority generation plans, and establish a profit maximization model; (2) propose a bidding decision-making linkage and segmented bidding optimization model for cascades upstream and downstream hydropower stations; (3) construct a model for decomposing the annual and monthly planned electricity consumption curves and developing operational plans for giant cascade power stations that are suitable for cross-provincial and cross-regional power transmission and transformation; (4) a runoff, electricity price, and market distribution model has been proposed, laying the foundation for further research on multi-scale optimization models for hydropower. Finally, prospects for research on the participation of large-scale hydropower systems in the electricity market are summarized, expecting to promote the marketization of large cascaded hydropower systems. The dispatching and trading of the TGLHS implies that it is important and necessary to explore market theories and methods considering hydropower characteristics and operation needs. [ABSTRACT FROM AUTHOR]

The remuneration scheme for the electricity produced by Waste-to-Energy (WtE) recovery plants has changed recently in Portugal according to 2020 legislation. The new model, linking the electricity remuneration from WtE plants to the spot electricity prices, is expected to bring greater uncertainty in the waste activity, which is a novelty for the sector. In Portugal, Valorsul is the municipal waste treatment entity responsible for the recovery and treatment of municipal solid waste (MSW) produced in 19 municipalities in the Lisbon area. This paper highlights the impact of the new Portuguese electricity remuneration scheme for electricity from waste on Valorsul's WtE plant. For this purpose, the new remuneration scheme is modeled and simulated based on electricity spot market price scenarios, which are compared with the base case scenario of the former remuneration scheme. Considering different electricity prices for the electricity produced by the WtE plant, the present study anticipates the consequences of the gate-fee of such regulatory changes. Results show that any price changes in the electricity remuneration scheme are offset by equivalent changes in the waste gate-fee. Consequently, the change in the remuneration of the electricity from the WtE plant is, in fact, neutral for the Valorsul accounts and lower revenues from the electricity generation activity of the WtE will negatively impact the gate-fee prices paid by the waste users. [ABSTRACT FROM AUTHOR]

China has made significant progress in developing renewable energy, but challenges in integrating variable sources like wind, solar, and hydro power persist. Existing research has a regional focus and lacks a comprehensive understanding of integration challenges in specific regions. Moreover, research concentrates on individual energy types rather than multiple sources. Addressing these challenges requires a deeper analysis of market rules, incentives, and mechanisms for integrating numerous renewable energy types. The existing research also overlooks crucial aspects like energy consumption, grid planning, and power market mechanisms. To promote renewable energy integration effectively, policymakers must address these interconnected factors through comprehensive investigations and research efforts. This study proposes tailored solutions to encourage integration in China based on international experiences, such as Brazil's contract markets and electricity redistribution mechanism, the Nordic spot market, and California's negative pricing mechanism. The strategies derived from global practices for China's renewable energy integration include diverse trading mechanisms, expanded contract markets, optimized local consumption, fortified grid infrastructure, and improved market mechanisms. [ABSTRACT FROM AUTHOR]

The energy sector plays a critical role in reducing emissions by transitioning to renewable energy. However, integrating renewable energy into the electricity market impacts market clearing. This article investigates market clearing decisions of renewable and traditional energy companies, considering electricity price segmentation. We propose an interprovincial bilevel market clearing decision-making model. The upper level optimizes the provincial market through a unilateral bidding model, minimizing the operating cost of the power market as the objective function. Meanwhile, the lower level optimizes power purchases by minimizing the cost of acquiring interprovincial renewable energy. A case study was conducted to determine the optimal interprovincial renewable energy purchasing power for multiple energy generation companies, considering the minimum market operating cost and segmented electricity prices. The proposed bilevel market clearing model captures the interplay between intraprovincial and interprovincial transactions, integrating electricity price and ecological attributes. By solving this model, we can attain optimal interprovincial electricity purchase of renewable energy, minimizing the overall market operating cost in the province. Through simulation, 18% of interprovincial transactions are needed to absorb 7.21 KWH of renewable energy from other provinces. When considering price segmentation, the cross-provincial two-level market clearing model can reduce the market cost by CNY 1,554,700. [ABSTRACT FROM AUTHOR]

Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models. [ABSTRACT FROM AUTHOR]

Fossil energy consumption and environmental protection issues have pushed electric vehicles (EVs) to become one of the alternatives to traditional fossil-fuel vehicles. EV refers to a vehicle that uses electric energy as power and is driven by an electric motor. The electric energy of EVs is stored in batteries. When the EV is not traveling, the battery can provide power for other loads. Therefore, with the increase in the number of EVs and the load of the power grid, the EV-to-grid (V2G) mode, which uses EVs to supply power to the power grid, has gradually entered the field of vision of researchers. The physical connection mode, charge and discharge technology, and energy management strategy are the main topics of the current review papers; however, there is a lack of systematic research on V2G modeling, framework, and business models. This paper describes the concepts of the spatio-temporal distribution model and the adjustable capacity of EVs. In addition, common constraints and methods in optimization are introduced. Moreover, this paper introduces the interactive relationship among power grids, load aggregators, and EV users. Furthermore, the business model of V2G is introduced and analyzed from various perspectives. Finally, the future development of V2G is pointed out. This paper's goal is to provide an overview of the present V2G application scenarios and to identify any challenges that must be overcome. [ABSTRACT FROM AUTHOR]

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