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Electric power systems are currently undergoing a transformation towards a decentralized paradigm by actively involving prosumers, through the utilization of distributed multi‐energy sources. This research introduces a fully decentralized multi‐community peer‐to‐peer electricity trading mechanism, which integrates iterative auction and pricing methods within local electricity markets. The mechanism classifies peers in all communities on an hourly basis depending on their electricity surplus or deficit, facilitating electricity exchange between sellers and buyers. Moreover, communities engage in energy exchange not only within and between themselves but also with the grid. The proposed mechanism adopts a fully decentralized approach known as the alternating direction method of multipliers. The key advantage of this approach is that it eliminates the need for a supervisory node or the disclosure of private information of the involved parties. Furthermore, this study incorporates the flexibility provided by residential heating systems and energy storage systems into the energy scheduling of some prosumers. Case studies illustrate that the proposed multi‐community peer‐to‐peer electricity trading mechanism effectively enhances local energy balance. Specifically, the proposed mechanism reduces average daily electricity costs for individual prosumers by 63% compared to scenarios where peer‐to‐peer electricity trading is not employed. [ABSTRACT FROM AUTHOR]

The emergence of variable renewable energy (VRE) technologies has created a range of different energy contracting techniques. Within Australia's National Electricity Market (NEM), Run‐of‐Plant (RoP) Power Purchase Agreements (PPAs) became the most common form of contract with purchasers of wind and solar energy agreeing to pay a fixed price for energy irrespective of when it is produced and, therefore, its actual value to the market. In November 2023, the Commonwealth Government adopted a 32 GW RoP PPA Contract‐for‐Difference (CfD) underwriting policy that aims to effectively shield the generator from market price risk. This article discusses different contract structures and their impact on participant behaviour during periods of material oversupply and negative prices. We find that embedded solar PV exports into the distribution network, which are not required to dynamically participate in the wholesale market, have increased wholesale energy supply, enabling profit maximising vertically integrated renewable firms to drive prices lower in a manner that partially strands the output of RoP PPA CfD generators with a $0/MWh price floor. A key conclusion from our analysis is that requiring embedded solar PV to effectively participate in the wholesale market appears to be a pre‐condition for the efficacy of government‐initiated RoP PPA CfDs. [ABSTRACT FROM AUTHOR]

The market coupling of adjacent market zones is a key element in terms of coping with rising levels of uncertainty in the European energy system due to the adoption of more volatile renewable energy sources. An adequate analysis of this issue yet requires an appropriate consideration of uncertainty along with a detailed modelling of market coupling. Thus, we present a novel approach combining a comprehensive unit commitment and dispatch model with a sophisticated methodology to reflect the uncertainty of wind power generation by considering forecast updates for the German market region in the market clearing process. Subsequently, we investigate the impact of uncertainty on cross‐border electricity flows in a European case study. Our results show that all adjacent market zones and all relevant technologies significantly contribute to balancing forecast updates in Germany, underlining the benefits of intraday market coupling. Further, we observe that a joint and unrestricted intraday market is the most cost‐efficient market solution. Finally, our analyses show that more extensive market coupling is indispensable for the further integration of renewable energy sources in the future. [ABSTRACT FROM AUTHOR]

Electricity price forecasting (EPF) is an emergent research domain that focuses on forecasting the future electricity market price both deterministically and probabilistically. EPF has attracted enormous interest from both practitioners and scholars since the deregulation of the power market and wide applications of renewable energy sources, such as wind and solar energy. However, forecasting the electricity price accurately and efficiently is an extremely challenging task because of its high volatility, randomness, and fluctuation. Although quantile regression averaging (QRA) has been demonstrated to be efficacious in probabilistic EPF since the global energy forecasting competition in 2014 (GEFCom2014), it is sensitive to nuisance variables especially when the number of variables is large. The forecasting accuracy will be negatively affected by these nuisance variables. To address these challenges, this study investigates a nonconvex regularized QRA in probabilistic forecasting. Two types of nonconvex regularized QRA select the important inputs obtained from point forecasting to obtain more accurate forecasting outcomes. To demonstrate the effectiveness of the proposed EPF model, two real datasets from the European power market are considered. [ABSTRACT FROM AUTHOR]

This study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting. [ABSTRACT FROM AUTHOR]

Rapid progresses concerning wearable electronics call for stretchable batteries. Owing to the merits of high energy density and wide operating voltage range, organic electrolyte‐based lithium‐ion batteries (LIBs) take a large proportion in the powering markets. Due to the high sensitivity to moisture and oxygen, it is difficult to obtain long‐term reliable stretchable organic electrolyte‐based LIBs when they are directly exposed to the air. Benefiting from the outstanding gas impermeability of liquid metal (LM) and relatively good barrier properties for reactive acid gas molecules by polyisobutylene (PIB), an LM‐PIB seal is demonstrated for organic electrolyte‐based LIBs. In the seal, LM can prevent the inward permeation of oxygen and water vapor, and PIB can block the outward permeation of hydrolysis acid generated from the side reactions during the operation of the organic electrolyte‐based LIBs, which helps prevent the detrimental interaction between the hydrolysis acid and LM. A specific capacity of over 120 mAh g−1 is achieved and the battery shows consistent electrochemical performances under multiple deformations. This study offers a different design approach for long‐term operation of stretchable organic electrolyte‐based LIBs. [ABSTRACT FROM AUTHOR]

Abstact: In the deregulated electricity market, it is increasingly important to accurately predict the fluctuating, nonlinear, and high‐frequent electricity price for market decision‐making. However, the uncertainties associated with electricity prices, such as non‐stationarity, nonlinearity, and high volatility, pose critical difficulties for electricity price forecasting (EPF). Unlike point forecasting, which provides only a single, deterministic estimate of future prices, probabilistic forecasting gives a more comprehensive and nuanced picture of future price dynamics, which can help market participants make better‐informed decisions when facing uncertainty. Therefore, in this paper, we propose a robust deep learning method for multi‐step probabilistic forecasting. First, we use the least absolute shrinkage and selection operator (LASSO) in the expert model to generate point forecasts. Second, we introduce the smoothly clipped absolute deviation regularization term, a nonconvex penalty with proven oracle properties in model selection, into temporal fusion transformers. Finally, we employ the proposed model to integrate point forecasts to give probabilistic forecasts. To evaluate the proposed forecasting model, real‐data experiments are conducted in the Nord Pool electricity market and the Polish Power Exchange market. Empirical results show that the proposed model has demonstrated superior probabilistic forecasting performances compared with other competitors and has proven its effectiveness in real‐world applications. [ABSTRACT FROM AUTHOR]

The creation of features makes the difference in improving the photovoltaic forecast (PVF) for on‐grid, hybrid and off‐grid PV systems. The importance of the PVF is tremendous, and it can be essential in optimizing the home appliances to maximize the Renewable Energy Sources (RES) usage or to create performant bids for the electricity market. Several use cases are considered from the connectivity point of view. Therefore, in this paper, we propose a Weather Prediction Error (WPE)‐based method that uses a Stacking Regressor (SR) for various PV systems that coexist in the emerging Energy Communities (EC) landscape. The novelty of the research we conduct consists in proposing several features and determining the coefficients to adjust the PVF based on WPE. The forecast results of four types of PV systems from size and connectivity point of view are investigated. Compared with individual Machine Learning (ML) models, R2 increases with more than 3% with the SR and with more than 6% after applying the adjustment coefficients. Nevertheless, the major improvement is recorded for the off‐grid inverter and for the large industrial PV power plant, demonstrating that the proposed model is suitable for these types of systems. The other metrics improved as well, especially Mean Average Error (MAE) that decreases between 10% and 23%. A significant decrease is in the case of the industrial on‐grid PV, from 130 kW to 123 kW using the SR and to 107 kW after adjustments. This represents around 18% from the initial MAE. The ratio of daily deviations is also improved using the SR. For all the PV systems, the ratio stabilizes in a shorter interval, from daily values between 0.78 and 1.33 obtained with the ML models to values between 0.83 and 1.24 obtained with the SR. After the final adjustments, the interval becomes shorter, having daily values between 0.90 and 1.10. [ABSTRACT FROM AUTHOR]

As one of NEDO's international demonstration projects, a large‐capacity hybrid battery storage system which consists of two types of battery cells with different charge‐discharge characteristics was constructed in the state of Lower Saxony in the Federal Republic of Germany. By using the hybrid battery system has a lithium‐ion which has an advantage in short time and high rate, and a sodium‐sulfur battery (NAS) which has an advantage in long time and low rate, the tender participation in the balancing power market in Germany had been executed. The hybrid battery storage system could successfully drive and provided services for primary and secondary control reserves, elimination of power imbalance, and reactive power injection, as well as a combination of these services. [ABSTRACT FROM AUTHOR]

We examine the impact of product market competition on firms' systematic risk. Using a measure of total product market similarity, we document a strong negative relationship between market power and market betas. The effect more than triples in the most recent period of low competition. Anticompetitive mergers result in a significant reduction in market betas. Firms facing less competition seem to be partially insulated from systematic discount‐rate shocks. Lower equity costs therefore imply that market power is partly self‐perpetuating. [ABSTRACT FROM AUTHOR]

I analyze secret bilateral forward contracting in a Cournot oligopoly. A secret bilateral forward contract affects the production quantities of the firms which are party to the contract but not of the outsiders. On the one hand, forward contracts facilitate for heterogeneous firms to rationalize production across facilities. On the other hand, firms also consider how forward contracting affects their combined production. I show that the spot market is less concentrated than the ownership of dispatchable facilities in the industry. Furthermore, the ownership distribution of nondispatchable facilities is irrelevant for consumer welfare. I discuss implications for policy in electricity markets. [ABSTRACT FROM AUTHOR]

Electricity, as a sustainable energy carrier, plays a central role in the transition scenarios for carbon neutralization of energy systems. Expanding the potential of electricity requires intelligent integration of electricity infrastructures and electricity markets with distributed energy resources (DERs) including roof‐top solar photovoltaics (PVs), controllable loads, and storage batteries. This integration will advance the management of power distribution grids, resulting in harmonization of the distribution grids and variable renewable generation. In the foreseeable future, the DERs will be actively used to perform advanced management of power distribution grids and compensate fluctuation in variable renewable power generation such as PVs and wind power. Market‐based coordination mechanisms are already practiced in large‐scale electricity production, electricity trading and transmission around the world. Using these mechanisms at the distribution grid level to integrate numerous flexible devices into the grids remains an important research topic. This paper reviews the role of DER integration toward carbon‐neutral power systems, the state‐of‐art and challenges associated with market‐based approaches for integrating flexible demand, distributed generation, and storage. Methods for assessing the value of DER aggregation such as virtual power plants and microgrids are also explored. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the gradual attention of global climate change, a series of carbon emission plans have been formally implemented in the field of energy. There is a consistent relationship between the carbon market and the electricity market in promoting clean energy development and improving carbon dioxide emission reduction targets. The market design of a low‐carbon power system should be considered as a whole to promote the coordinated development of carbon and power markets. Taking an industrial park in China as the research object, an internal market trading model of an industrial park considering carbon dioxide emission reduction targets is proposed. The proposed trading mechanism considers both carbon dioxide emission reduction targets and social welfare maximization. The proposed market trading model connects day‐ahead and intraday electricity markets, which deal with uncertainty about renewable energy sources. [ABSTRACT FROM AUTHOR]

We estimate market power in California's surface water market. Market power may distort the potential welfare gains from water marketing. We use a Nash‐Cournot model and derive a closed‐form solution for the extent of market power in a water market setting. We then use this solution to estimate market power in a newly assembled dataset on California's water economy. We show that, under the assumptions of the Nash‐Cournot model, market power in this thin market is limited. [ABSTRACT FROM AUTHOR]

Here, a novel tri‐level energy market model aimed at addressing the challenges posed by demand side management (DSM) in the electricity distribution company (EDC) is introduced. DSM has emerged as a new strategy employed by EDCs to manage and control electricity demand by encouraging end‐users to modify their electricity consumption patterns. This is achieved through the participation of demand response (DR) aggregators, which play a crucial role in assisting end‐users with strategies and technologies to reduce their electricity consumption during peak hours. The proposed tri‐level energy market model consists of four distinct players: EDC, microgrids, aggregators, customers. The interactions between these four actors are modelled within a tri‐level game framework, where the EDC and aggregators act as leaders, and the micro‐grids and customers are followers. This multi‐level and multi‐player game structure allows for a more realistic representation of the complexities involved in DSM programs within the energy market. To demonstrate the effectiveness of the proposed model, a real case study is utilized, showing that the new model better resembles real‐life market conditions. The results illustrate how the tri‐level energy market model can significantly reduce demand fluctuations during peak hours, leading to improved efficiency and effectiveness within DSM programs. [ABSTRACT FROM AUTHOR]

In recent years, as a result of emerging renewable energy markets, several developed regions have already launched Real‐Time Pricing (RTP) strategies for electricity markets. Establishing optimal pump operation for water companies in RTP electricity markets presents a challenging problem. In a RTP market, both positive and negative electricity prices are possible. These negative prices create economically attractive opportunities for Water Distribution System (WDS) to dispatch their energy consumption. On the other hand, excessively high prices may put WDS at risk of supply disruptions and reduced service levels. However, the continuous development of wind power and photovoltaics results in more volatile and unpredictable fluctuations in the price of renewable energy. The risk arising from uncertainty in electricity prices can lead to a significant increase in actual costs. To address this issue, this paper develops an a posteriori random forest (AP‐RF) approach to forecast the probability density function of electricity prices for the next day and provide a risk‐constrained pump scheduling method toward RTP electricity market. The experimental results demonstrate that the developed method effectively addresses the issue of increased costs caused by inaccurate electricity price forecasting. Plain Language Summary: With the emergence of renewable energy markets in recent years, several developed regions have introduced Real‐Time Pricing (RTP) strategies for their electricity markets. This has created a difficult challenge for water companies seeking to establish the optimal pump operation in RTP markets. This study investigates the use of a risk‐constrained optimization scheduling approach for water distribution networks to mitigate the risks associated with inaccurate real‐time electricity price forecasting. Our proposed method is designed to reduce the costs associated with inaccurate electricity price prediction. Key Points: A robust pump scheduling approach toward real‐time electricity price market is developedDeveloping a posteriori random forest algorithm to predict the probability density function of Real‐time electricity priceOptimal scheduling with risk constraints is an effective approach to mitigating the risks associated with inaccurate electricity forecasting [ABSTRACT FROM AUTHOR]

China's electricity spot market is undergoing rapid development. With the increasing integration of new energy sources, it has become imperative for new energy power plants to participate in the market actively. Simultaneously, new energy power facilities are equipped with energy storage to cope with the randomness of power generation. In order to address the optimization and control challenges of wind‐storage combined systems in the market environment, a charging and discharging strategy is proposed. Considering system parameters as variables, an online rolling‐horizon optimization model for wind‐storage systems is constructed to maximize revenue. Case studies demonstrate that this strategy effectively improves the overall revenue of wind power plants. After optimization, the return on investment (ROI) of the system increases by 2.29%. The selection of system parameters can significantly impact the outcomes. The optimal values of the capacity configuration and step size are 25 MWh and 45 min. [ABSTRACT FROM AUTHOR]

Monthly unit commitment (UC) is a decision‐making process that determines the optimal generation scheduling of mid and long‐term power system, often operating as a subproblem within monthly electricity market operations and long‐term maintenance scheduling optimization in China. Dealing with transmission constraints in monthly security‐constrained unit commitment (SCUC) is considered to be challenging, but its simplification can be achieved through eliminating inactive security constraints. Additionally, the uncertainty associated with wind power needs to be given due consideration in monthly scheduling. Here, a three‐step watchlist construction strategy is proposed to facilitate the rapid screening of overloaded power flow constraints in monthly unit commitment, including three lists (i.e. list of risk, list of concern, and list of interest) under different conservatism. Importantly, the shift factor method is utilized to screen the potential overloaded constraints arising from the redispatch process for hedging the effects of large wind uncertainties. Results obtained from the testing of IEEE‐118 system indicates that more than 99% of active power flow constraints could be successfully screened out when wind power fluctuation is at 20%. As such, the proposed scheme provides an effective method to consider uncertainty associated with large‐scale wind power generation integrated into the power grid. [ABSTRACT FROM AUTHOR]

The article by Naugle et al. explores the core characteristics of system dynamics modeling and the need for a clear definition. The authors argue that system dynamics modeling should focus on addressing complex problems and improving decision-making. They propose a modified definition that includes elements such as causal structure, data-informed models, and replicable predictions. The article also emphasizes the importance of collaboration with other methods and the integration of qualitative and quantitative approaches. The authors call for further research and development in both qualitative and quantitative modeling to advance the field of system dynamics. [Extracted from the article]

Demand‐side flexibility or demand response (DR) has long been recognized as a potential source of balancing capacity. The charging of electric vehicles (EVs) is a known source of flexible capacity, and the vast amount of charging capacity available can be utilized for valuable applications, including ancillary power markets, by controlling the charging sessions according to the needs of the power system. One identified and unexplored potential market for EV DR capacity is the Fast Frequency Reserve (FFR) market for the Nordic system area. This article proposes and demonstrates that a home charger can participate in this extremely fast reacting ancillary service market with only software modifications made to the charger. The biggest challenge in the FFR market is the response time requirement for capacity activation. When a certain frequency limit is exceeded, the activation must happen within 0.7 s in the worst case. In this study, a laboratory test setup was constructed to test the capabilities of the FFR‐enabled EV charger. The tests were conducted by building a laboratory microgrid capable of changing the grid frequency. Additionally, an economic feasibility study was carried out to evaluate the business potential of the concept in Finland. The economic study included an analysis of the concept's potential with different levels of DR service availability. The laboratory tests demonstrated the FFR capabilities of the charger and as a final outcome of the research, the Norwegian transmission system operator (TSO) approved the tested charger as a type‐prequalified FFR‐providing entity. The economic study revealed that the approach has good business potential, primarily because of the almost non‐existent cost side even if the availability rate decreases significantly. [ABSTRACT FROM AUTHOR]

Shared electric vehicles (shared EVs) could be treated as energy storage and participate in electricity market services. In that case, the schedulable capacity that shared EV provide to grid in future time needs to be predicted accurately. Based on open data provided by a shared vehicle rental project, this research firstly proposes a method to build up schedulable capacity data set to prepare sample data for the deep learning‐based forecasting task. Secondly, a schedulable capacity forecasting model by MAML‐CNN‐LSTM‐Attention is proposed. Through the model, ultra‐short prediction (1 h) of aggregated schedulable vehicle‐to‐grid capacity of shared EVs is performed. The proposed model uses model‐agnostic meta‐learning (MAML) to optimize initial parameters of network to quickly adapt to changes in characteristics caused by different travel habits of different functional communities; uses two‐layer convolutional neural network (CNN) with long short‐term memory neural network (LSTM) and Attention mechanism to extract temporal‐spatial features of schedulable capacity for important historical moments. Simulation results show that the proposed model has a mean absolute percentage error (MAPE) of 1.13% and goodness of fit of 0.976, the MAML decreases the MAPE by 1.04%, its stability and accuracy were better than previous used models. The research can act as useful decision aids for shared EV operators to participate in market services. [ABSTRACT FROM AUTHOR]

In recent years, machine and deep learning models have attracted significant attention for electricity price forecast in global wholesale electricity markets. Yet, a predominant focus on point forecast in most parts of literature limits the practical application of these models due to the absence of uncertainty quantification. In this study, we first perform an analysis of the electricity price trends in the Mexican wholesale electricity market to determine the influence of key variables. Using independent component analysis and wavelet coherence analysis, we were able to identify primary determinants influencing locational marginal electricity prices. Subsequently, we applied four different models covering the most important algorithms proposed in the literature for electricity price forecast. Our findings revealed that the most accurate forecasting results were achieved using a deep learning‐based method with a decision tree‐based model trailing closely. Finally, we incorporate conformal prediction for uncertainty quantification by calculating the prediction intervals with a target coverage level of 95%. The conformal prediction intervals provide a more comprehensive view of the possible future scenarios, enhancing economic efficiency, risk management, and decision‐making processes. This is particularly important because of the dynamic nature of electricity markets, where prices are strongly influenced by multiple factors. [ABSTRACT FROM AUTHOR]

In electricity markets, generators are rewarded for providing energy and for enabling grid reliability. The two functions are compensated separately: with energy market payments and ancillary services market payments. We provide evidence of changes in the generation mix in the energy market that are driven by exogenous changes in an ancillary services market. We provide a theoretical framework and quasi‐experimental evidence for understanding the mechanism: it results from the multi‐product nature of power plants combined with discontinuities in costs. Although much research focuses solely on the energy market, our results suggest that spillovers between the two markets are important. [ABSTRACT FROM AUTHOR]

We study a novel database of intellectual property (IP) licensing agreements sourced from filings made by publicly listed corporations, a large fraction of which firms (initially) disclose with redacted terms. In contrast to the benchmark that IP quality alone determines the pricing of IP, we argue that bargaining power between licensing counterparties plays a critical role in explaining several patterns in observed royalty rates. Licensors with differentiated technology and high market power charge higher royalty rates, while larger‐than‐rival licensees pay lower royalty rates. Licensors command premium royalty rates for contract exclusivity, especially in competitive markets. Finally, we employ this framework and setting to understand the pricing implications of nondisclosure: licensors redact payment terms when they transact at lower royalty rates, consistent with preserving bargaining power for future negotiations. Our findings offer a new explanation for innovator secrecy and have several practical takeaways for transfer pricing and patent litigation. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) can aggregate distributed energy resources (DER) and controllable loads to participate in the bidding of electricity market and carbon trading market. However, the uncertainty of renewable power production brings high transaction risks to VPP. Given this background, this paper proposes a novel Wasserstein‐distance based two‐stage distributionally robust chance constrained (DRCC) bidding model for VPP participating in the electricity‐carbon coupled market. The uncertainties are modelled as an ambiguity set based on Wasserstein distance, in which the two‐sided chance constraints are guaranteed satisfied. In the first stage, VPP's revenue is maximized according to the forecast information. In the second stage, the re‐dispatch measures are determined to hedge against the perturbation of uncertainties under the worst‐case distribution within the ambiguity set. Finally, a reformulation approach based on strong duality theory and conditional value‐at‐risk (CVaR) approximation is proposed to transform the DRCC problem into a tractable mixed‐integer linear programming (MILP) framework. Case studies are carried out on the IEEE 30‐bus system to verify the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Assuming the bidding on balancing market of photovoltaic (PV) system, critical issues are adequate determination of amount of bid and decision making on whether to make a bid or not. This study proposes a rule‐based scheme handling this problem regarding the trade‐off between opportunity loss and penalty event where the actually generated power becomes less than the bid. The amount of bid is adjusted by utilizing trend information of PV output error, and the execution of bidding is decided based on probability of exclusion from the market calculated using queueing theory. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The current energy crisis calls for a rapid transition to renewables. Solar photovoltaics (PV) is the key component of the future energy system and a majority of PV systems are installed on rooftops in Europe. This article studies the business case of prosumers, i.e., consumers who produce part or all of their electricity with PV. The levelized cost of PV electricity is compared with the average value of PV electricity for the prosumers in residential, commercial, and industrial installations in Finland, Germany, France, Italy, and Spain with different self‐consumption (SC) and weighted average cost of capital rates. Prosumer business cases are positive in every studied market segment and country, especially for industrial PV systems with 100% SC where a payback time of less than 4 years can be achieved. The sensitivity of payback time on key input parameters is also studied. Reasonable cost of capital and adequate SC of PV generation remain the key elements for attractive prosumerism. [ABSTRACT FROM AUTHOR]

Emergence of different stakeholders with contrary objectives in active distribution network (ADN) requires for a fair network expansion planning that considers all of the contrary objectives. This paper provides an expansion planning model for ADN strategic visioning from electric distribution stakeholders' organization (EDSO) perspective, which takes into account the objectives of all stakeholders. The stakeholders are network owner, active customers (ACs), consumers, and environmental regulatory body (ERB). The proposed model is multistage bi‐level, where the upper‐level minimizes the present value of the total costs imposed to all ADN stakeholders and the lower‐level minimizes the total generation costs within the distribution‐level electricity market. The Karush–Kuhn–Tucker optimality conditions and the linearization techniques are employed through which the model is converted to mixed integer linear programming which can be tackled effectively. The model provides a visionary expansion plan that policymaker must legislate to realize it. Effectiveness of the model in providing a visionary expansion plan considering EDSO's perspective in making cost effective planning decisions per social welfare goals is verified through several case studies on 33‐bus standard test system. According to the simulation results, transition from DSO to EDSO in the presence of low‐capacity ACs improves social welfare by 5%. But this improvement is up to 70% in the presence of high‐capacity ACs. [ABSTRACT FROM AUTHOR]

This paper investigates how credit information sharing affects corporate trade credit financing. Utilising the difference‐in‐differences method, we find a significant reduction in trade credit for infra‐marginal bank borrowers following the introduction of the Chinese National Enterprise Credit Information Publicity System (NECIPS). To reveal the mechanisms underlying the reduction in trade credit, we show that the NECIPS alleviates information asymmetry and increases formal finance access. This financing substitution effect is magnified by weak bargaining power in product markets. Our findings complement the literature on the determinants of trade credit and underscore the indispensable role of the public credit registry. [ABSTRACT FROM AUTHOR]

To realize Carbon Neutrality, renewable energy would be the main source of electricity, such as photovoltaic (PV) power generation in the Japanese power system. However, a decrease in the number of operating thermal generators, the main source of balancing power in the Japanese balancing market, is causing concerns regarding a shortage of balancing power in the future. PV can therefore provide balancing power in addition to energy; thus, the PV power economic value in electricity markets will become diversified. A solution examination and rationality analysis of technical issues for PV‐based balancing power creation are needed. Here, by applying the proposed headroom control method based on the PV output curtailment beforehand, PV generator optimizes benefit by bidding into the balancing market when the spot market price is very low. Optimizing the bidding strategy on the spot market and balancing market is conducted, and battery integration is also considered. The optimization simulation results validate the possibility of creating balancing power by a PV power producer, and the total revenue valuation verifies a more beneficial effect. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This research investigates the short‐term (ST) forecasting performance of the daily prices of the Indian exchange‐traded day‐ahead (DAM) market, divided into 13 bid areas, each consisting of states with varied fundamentals. Forecasts are built employing SARIMA (seasonal autoregressive integrated moving average) and MLP (multilayer perceptron) methods. Moreover, the robustness and performance of the model is compared using the lowest error and the Diebold–Mariano (DM) test statistic values. The results indicates that the SARIMA model has high prediction accuracy with error values ranging from 1% to 5% with Southern region having the highest error of 4.53% and Northern having the least error of 1.27%. However, validation by the DM test suggests no statistical significant difference between the two models. The power generators, distribution companies, traders, policymakers, strategists and managers could use the findings for effective power management through proper planning. [ABSTRACT FROM AUTHOR]

Electricity markets are facing innovation disruptions at levels never seen before. One of the major drivers is the introduction of distributed energy resources (DERs) technologies, especially those that apply to rooftop solar power. This has called into question established regulatory practices and brought new challenges to system operations. In this context, this article seeks to provide a comprehensive investigation of potential policy options for Australia's energy markets from two perspectives. We first examine recent and proposed reforms in this area, and then proceed to review the challenges for Australian regulators that will have major implications for the future, affecting policymaking, regulation, and market design. Points for practitioners: The Australian energy framework is complicated, reflecting its regulatory history, geographical complexity, and federal governance structure.As a force for creative distraction, the penetration of DERs has created challenges for the electricity system and requires careful regulatory treatment.Topical themes in the regulation of DERs include, amongst others, technical standards, metering access, reserve capacity, new business models, network stability, the role of regulated utilities, and support for low‐income households.The necessary regulatory transition is accepted as being evolutionary rather than revolutionary in nature so as to enable the existing major stakeholders to adapt to the new environment. [ABSTRACT FROM AUTHOR]

With the introduction of dual carbon targets, clean energy is connected to the grid on a large scale, and the stochastic and volatility of its output further affects the stable operation of the new power system. Meanwhile, in order to solve the problem that it is difficult to take into account the participation of clean energy in market‐oriented trading and complete consumption in the construction of power market, the new type of power system source–load balance dispatching model considering the market of clean energy and cross‐section stabilization is proposed. Firstly, a clean energy market price competition model based on the market clearing mechanism is proposed to realize the alternative consumption of clean energy. Secondly, considering the section security of inter‐system power transmission, a new power system source‐load balance two‐level optimization model is established by considering the cross‐section power rate and real‐time trading of clean energy market. Finally, the advantages of the model in enhancing the high proportion of clean energy consumption, rationally clearing the clean energy market and reducing the cross‐section load ratio of the contact line are verified through simulation. [ABSTRACT FROM AUTHOR]

The present paper simulates cyberattacks in DIgSILENT for energy management and state estimation. Assuming hackers have adequate information to alter input data, they can alter the power market and the available transmission capability (ATC). Detection programs may fail if false information is coordinated in the presence of error information. The minimum number of network measurements before the state estimation program converges is 54% of the total measurements. In the new method, weighted least squares improve the state estimation speed. The new method can also improve the speed of ATC. In addition to identifying the cyberattack, the algorithm in the present paper can be used to identify the target of the attack. It also uses complete and correct information and even incorrect measurements to report state estimator errors. The present paper shows that ATC values follow the inverse pattern of load and loss. A real sample network is presented at the end of the paper to test the suggested method. [ABSTRACT FROM AUTHOR]

Reducing loss and providing a secure power supply for customers have always been the main goal of distribution systems planners. Recently, the development of competitive electricity markets and responsive loads participation in such markets together with the integration of regional energy networks (RENs) have resulted in creating some challenges for RENs' owners to increase operating efficiency, and reduce investment costs. In this paper, to overcome these problems, a new modeling for coordinated planning of distribution networks and RENs in the presence of responsive loads is proposed and to achieve the optimal solution of the problem, the genetic algorithm is used. Operation uncertainties due to the installation of wind turbines and photovoltaic resources in RENs are considered. The probability‐tree method is used to generate and model operation scenarios and their variable production, respectively. To validate and confirm the efficiency of the proposed model, numerical studies were applied on a 25‐bus Institute of Electrical and Electronics Engineers standard distribution network, including two RENs equipped with responsive loads. The results show the fact that the flexibility of the demand side in the electrical sector due to the activity of responsive loads causes to increase in the installation capacity of energy resources and storages. [ABSTRACT FROM AUTHOR]

As stationary hybrid energy‐storage systems (HESS) for power systems applications have recently drawn interest due to their enhanced performance and decreasing cost, developing systematic approaches for HESS design while considering controls is gaining traction. Herein, a method is presented to optimally design hybrid battery storage by proposing a mathematical modeling framework, formulated as a mixed integer linear programming model. The optimization is capable of handling multiple subsystems of batteries, considering their economic and technological performance. Decisions involve sizing of the batteries, optimal temporal and strategic dispatch to end uses, and energy sources for charging each battery. The applicability of the model is tested on four case studies for three battery chemistries representing distinct objectives: high‐power, high‐energy, and second life. Compared to traditionally designed battery storage with a homogeneous battery, optimally designed hybrid systems can save 12%–26% of system costs, depending on the nature of the dispatch profile. Findings point to design preference toward the second life battery supplemented with some high‐power or high‐energy battery capacity, or both. With the utilized electricity price structure, customers can experience approximately 10%–35% reduction in their bills. [ABSTRACT FROM AUTHOR]

Reactive power management (RPM) in electric power systems is usually based on a rule‐based control derived from the transmission system operator's (TSO) experience. This approach faces challenges as the number of decisions and the complexity of the system operation is increasing. With the increasing generation from renewables and the evolution of electricity markets, the available resources must be optimally utilized. This paper compares the optimization‐based approach (OBA) and the experience‐based expert approach (EBA) for RPM. The OBA is based on security‐constrained optimization with minimum redispatch cost as the objective function for a reference contingency. In contrast, the EBA's actions are based on the system operator's experience. Comparison is made in terms of the generator redispatch cost, active and reactive power redispatch volume, active power losses, nodal voltages, and the number of actions to ensure secure operation. The analysis using a reduced model of an actual transmission system shows that OBA is more beneficial than EBA, with up to 22% and 42% reduction in redispatch cost and volume, respectively. Moreover, the control decisions from both approaches are seen to be similar. This study aims to show the usefulness of the OBA and motivate TSOs to move towards optimization‐based reactive power management. [ABSTRACT FROM AUTHOR]

Recently, the participation of wind sources in electricity markets has become a severe challenge due to their intermittent nature. Reconfiguration of power systems can effectively reduce the negative effects of uncertainties. So, this article presents a new method for participating in wind power plants and uncertain customers in a day‐ahead electricity market considering the reconfiguration process. This method tries to maximize social welfare through a two‐level optimization problem. To this end, uncertainties are modeled using the empirical cumulative distribution function and the Monte–Carlo method, and a probabilistic analysis of the market is performed. Then, by defining some indices to evaluate the participants' satisfaction and using the analytic hierarchy process method (AHP), a new objective function is proposed so that its minimization leads to planning the system configuration and market participants to optimize mentioned indices. The proposed methodology also assumes that the participation of uncertain participants in the spot market will eliminate the imbalances caused by uncertainties. The simulations are implemented using real data on an 8‐bus sample network. The results confirm the efficiency of the proposed method in significantly reducing power producers' and customers' costs along with increasing total income and profit from the sale of energy. [ABSTRACT FROM AUTHOR]

In current power markets, the bulk of electricity is sold wholesale and transported to consumers via long‐distance transmission lines. Recently, decentralized local energy markets have evolved, often as isolated networks based on solar generation. We analyze strategic pricing, investment, and welfare in local energy markets. We show that local energy markets yield competitive equilibrium prices and provide efficient investment incentives. Yet, we find that strategic behavior leads to allocative inefficiency. We propose a clearing mechanism that induces truth‐telling behavior and restores first‐best welfare. [ABSTRACT FROM AUTHOR]

We study the investment effects of market integration on renewable energy expansion. Our theory highlights that market integration not only improves allocative efficiency by gains from trade but also incentivizes new investment in renewable power plants. To test our theoretical predictions, we examine how recent grid expansions in the Chilean electricity market changed electricity production, wholesale prices, generation costs, and renewable investments. We then build a structural model of power plant entry to quantify the impact of market integration with and without the investment effects. We find that the market integration in Chile increased solar generation by around 180%, saved generation costs by 8%, and reduced carbon emissions by 5%. A substantial amount of renewable entry would not have occurred in the absence of market integration. Our findings suggest that ignoring these investment effects would substantially understate the benefits of market integration and its important role in expanding renewable energy. [ABSTRACT FROM AUTHOR]

The integrated electricity and heat system (IEHS) is an emerging demand‐side flexible resource for power systems. IEHS operators participating in electricity markets considering their capabilities in reserve provision will face the reserve deliverability risk due to the energy‐limited storage nature of heat systems. To address this challenge and increase profitability, a distributionally robust joint chance‐constrained mechanism with enhanced quantifications is adopted for the heating system and reserve deployment uncertainties. Detailed pipeline storage representation for thermal networks and integrated demand response are incorporated into this strategic participation model. A two‐stage distributionally robust joint chance constrained program is then incorporated to effectively manage the reserve deliverability risk by addressing uncertainties from local distributed energy resources and real‐time reserve requests. The L‐shaped algorithm is then customized by incorporating bi‐linear Benders' decomposition and modified scenario filtering method to efficiently tackle solution challenges for the sophisticated model. Numerical results show the advantages of our approach in virtual thermal storage utilization, risk management, computational performance enhancement and scalability. [ABSTRACT FROM AUTHOR]

Renewable energy generation cannot be consistently predicted or controlled. Therefore, it is currently not widely used in the electricity market, which requires dependable production. In this study, reliability‐ and variance‐based controls of energy storage strategies are proposed to utilize renewable energy as a steady contributor to the electricity market. For reliability‐based control, photovoltaic (PV) generation is assumed to be registered in the power generation plan. PV generation yields a reliable output using energy storage units to compensate for PV prediction errors. We also propose a runtime state‐of‐charge management method for sustainable operations. With variance‐based controls, changes in rapid power generation are limited through ramp rate control. This study introduces new reliability and variance indices as indicators for evaluating these strategies. The reliability index quantifies the degree to which the actual generation realizes the plan, and the variance index quantifies the degree of power change. The two strategies are verified based on simulations and experiments. The reliability index improved by 3.1 times on average over 21 days at a real power plant. [ABSTRACT FROM AUTHOR]

This paper aims to study the predictive power of market herding effect for market volatility. We extend the widely used four types of linear HAR models by incorporating the market herding index and further introduce a nonlinear forecasting mechanism by using two machine learning algorithms—support vector regression (SVR) and random forest (RF). All the methods are applied to the Chinese stock market. We evaluate their in‐sample and out‐of‐sample performances for various prediction step lengths. The results show that according to the RMSE and MAE, for the daily volatility prediction, the extra herding factor makes no significant contribution, and the linear model has no significant difference with the algorithms as well, whereas with the increase of step length, the herding factor shows a significant power in the weekly, monthly, and quarterly prediction, and meanwhile, the RF algorithm outperforms the other methods. However, for the QLIKE, the herding factor always significantly improves the predictions for all kinds of step lengths and methods, and the SVR algorithm with the herding factor is dominant. Overall, the herding factor helps forecast the volatility, and the extent to which the precision is improved takes on an inverted "U" shape with the increase in step length. The two algorithms are superior to the linear model, especially for the longer‐term prediction, and the herding factor can help enlarge this advantage, which implies a nonlinear relationship between the herding and volatility. Besides, the extra liquidity factor plays no significant role in the volatility prediction, but the continuous variation (CV), jump variation (JV), and leverage effect are helpful in most cases. [ABSTRACT FROM AUTHOR]

Landscape decisions are multi‐faceted. Framing landscape decision‐making as a governance process that requires a collective approach can encourage key stakeholders to come together to co‐inform a discussion about their priorities and what constitutes good governance, leading to more holistic landscape decisions.In this paper, we recognise that a suite of complementary and multi‐dimensional approaches are in practice used to inform and evaluate land use decisions. We have called these approaches 'lenses' because they each provide a different perspective on the same problem. The four lenses are (i) power and market gain, (ii) ecosystem services, (iii) place‐based identity and (iv) ecocentric. Each brings a different set of evidence and viewpoints (narrative, qualitative and experiential, as well as quantitative metrics such as monetary) to the decision‐making process and can potentially reveal problems and solutions that others do not.Considering all lenses together allows dialogue to take place which can reveal the true complexities of landscape decision‐making and can facilitate more effective and more holistic decisions. Employing the lenses requires governance structures that give equal weight to all lenses, enable dialogue and coexistence between top down and bottom‐up approaches, and permit adaptation to local and granular place‐specifics rather than developing "one‐size‐fits‐all" solutions.We propose that formalising the process of balancing all the lenses requires public participation, and that a lens approach should be used to support landscape decisions alongside a checklist that facilitates transparency in the conversation, showing how all evidence has been considered and critically assessed. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

The development of foreign electricity markets has been relatively complete, while China's electricity market started late and the design of the market mechanism needs to be improved, so the selection of a suitable market model and the design of the market mechanism in hydropower‐rich regions is conducive to the healthy development of the electricity market. While most of the first eight pilot regions in China have entered the long‐period continuous operation stage, and all the second batch of six power spot pilots have started the simulation trial run. However, the construction of power markets in hydropower‐rich regions of China, Sichuan, and Yunnan, still faces multiple problems to be solved, such as ensuring the reliability of medium‐ and long‐term power supply, improving the efficiency of hydropower resources utilization and consumption scale, and maintaining the stability of market prices. In this paper, the authors establish a multi‐scale joint clearing model for different market models considering both long‐term and short‐term operation strategies, and conduct simulations of different market models at different time scales to compare and analyze the optimization results and electricity price fluctuations of different market models. It can stabilize the market price and reasonably reflect the true value of hydropower, and also promote the consumption of clean energy and guarantee the reliability of power supply in the medium and long terms. Finally, suggestions are made for the selection of electricity market models, the security of energy supply and the design of sustainable development mechanisms for hydropower in hydropower‐rich regions such as Yunnan and Sichuan in China, with a view to providing useful references for the construction of China's electricity market. [ABSTRACT FROM AUTHOR]

Two extreme cases of static stability are voltage stability and power angle stability. The general stability analysis only focuses on one of the voltage stability or power angle stability. The stability limit and state change are studied for a certain kind of stability characteristics. In this paper, the stability limits of static voltage and power angle are derived, respectively. Therefore, different stability characteristics and their interrelationships can be further studied. The index to judge the dominant stability characteristic is defined. The proposed index only needs the system wide‐area measurement information to quickly and accurately judge the dominant static stability characteristics. The dominant instability (DI) characteristic index proposed in this paper is equal to 1 or −1, which can help to quickly identify whether the power angle stability or voltage stability is dominant. The effectiveness of the method is proved by a simple system. [ABSTRACT FROM AUTHOR]

Microgrids can be considered a reduced power system network wherein the generation, transmission, and distribution of power occurs within a limited geographical area. They are employed to use the maximum penetration of renewable energy sources (RES). Microgrid also has advantages like reduction of transmission losses and costs pertaining to such losses. The objectives of economic dispatch, emission dispatch, penalty‐factor (PPF) based combined economic emission dispatch (CEED) and environmental constrained economic dispatch (ECED) are evaluated in this paper. A low‐voltage microgrid system for three different scenarios is studied. A novel, robust and powerful hybrid swarm‐intelligence optimization algorithm is used, which is based on amalgamating the features of conventional gray‐wolf optimizer, sine‐cosine and crow search algorithm. Due to the use of RES, the active power production cost and carbon emission of the microgrid system were reduced to approximately 3% for all the objectives. More than 15% reductions in generation costs were obtained throughout the study when the time‐of‐usage electricity market pricing strategy was used instead of the fixed pricing strategy. Also, results show that the ECED methods of economic emission dispatch provided better‐compromised solutions than the PPF‐based CEED method. Finally, non‐parametric statistical analysis is used to analyze and validate the quality of the projected novel technique, which is measured against the outcomes found with algorithms used in the state‐of‐art literature. [ABSTRACT FROM AUTHOR]

Energy Storage Resources (ESRs) can help promote high penetrations of renewable generation and shift the peak load. However, the increasing number of ESRs and their features different from conventional generators bring computational challenges to operations of wholesale electricity markets. In order to improve the computational efficiency, this paper tightens the generic ESR formulation for unit commitment. To avoid the complexity caused by ESR operations in both discharge and charge directions, a novel "decoupled analysis" is conducted to analyze one direction at a time. For each direction, ESRs over two and three time periods are categorized into several types based on their parameters. For each type, our recent four‐step systematic formulation tightening approach is used to construct the corresponding tight formulation. In order to consider more periods without analyzing all the drastically increased number of types, a series of major types are selected based on how many periods an ESR is able to discharge (charge) consecutively at the upper power limit. A related generic form of tight constraints over multiple periods is established. Moreover, validity and facet‐defining proofs of our tight constraints have been provided. Numerical testing results illustrate the tightening process and demonstrate computational benefits of the tightened formulations. [ABSTRACT FROM AUTHOR]

A review of the state‐of‐the‐art in short‐term Solar Power Forecasting (SPF) methodologies is presented in this paper. Over the last few years, developing and improving solar forecasting models has been the main focus of researchers, considering the need to efficiently increase their forecasting accuracy. Forecasting models aim to be used as an efficient tool to help with the stability and control of energy systems and electricity markets. Intending to further comprehend the factors affecting the quality of SPF models, this paper focuses on short‐term solar forecasting methodologies since they pose a crucial role in the daily operation and scheduling of power systems, since they focus on forecasting horizons typically ranging from 1 h to 1 day. The reviewed works are classified according to the climatic conditions, technical characteristics, and the forecasting errors of the different methodologies, providing readers with information over various different cases of SPF. Considering the need to improve the SPF efficiency, such classifications allow for important comparative conclusions to be drawn, depending on the location of each case and the meteorological data available. Future directions in the field of short‐term solar power forecasting are proposed considering the increasing development of SPF models' architecture and their field of focus. [ABSTRACT FROM AUTHOR]

Electricity demand forecasting plays a vital role in power system planning, operation, transmission design, and financial risk management. In this study, we employ a Gaussian Process model to estimate the probability distributions of short‐term electricity demand. To account for input uncertainty in multi‐period ahead forecast, we adopt an error propagation procedure that improves upon the naive recursive forecasting. This method is shown to perform well in terms of multi‐step ahead point and probabilistic forecasts. We illustrate the usefulness of the suggested estimator with an application to short term demand forecasting of Texas electricity market. [ABSTRACT FROM AUTHOR]