Search

Hydrogen production through renewable energy-powered electrolysis is pivotal for fostering a sustainable future hydrogen market. In the electricity sector, hydrogen production bears an additional demand that affects electricity price, and mathematical models are needed for the joint simulation, analysis, and planning of electricity and hydrogen sectors. This study develops a Cournot and a perfect competition model to analyze the links of the electricity and hydrogen sectors. In contrast to other solving methods approaches, the Cournot model is solved by convex reformulation techniques, substantially easing the resolution. The case studies, focusing on the Iberian Peninsula, demonstrate the region's potential for competitive hydrogen production, and the advantages of perfect competition to maximize the use of renewable energies, in contrast to Cournot's oligopoly, where the exercise of market power raises electricity prices. Sensitivity analyses highlight the importance of strategic decision-making in mitigating market inefficiencies, with valuable insights for stakeholders and policymakers. • A Cournot and perfect competition models to analyze the electricity and hydrogen markets are proposed. • Convex reformulation techniques are used for efficient model resolution. • Renewable energy is maximized under perfect competition, while market power exercised under Cournot leads to higher prices. • Case studies on the Iberian Peninsula demonstrate the region's potential for competitive hydrogen production. [ABSTRACT FROM AUTHOR]

This study aims to carry out a techno-economic analysis of different hydrogen supply chain designs coupled with the Swedish electricity system to study the inter-dependencies between them. Both the hydrogen supply chain designs and the electricity system were parameterized with data for 2030. The supply chain designs comprehend centralised production, decentralised production, a combination of both, and with/without seasonal variation in hydrogen demand. The supply chain design is modelled to minimize the overall cost while meeting the hydrogen demands. The outputs of the supply chain model include the hydrogen refuelling stations' locations, the electrolyser's locations and their respective sizes as well as the operational schedule. The electricity system model shows that the average electricity prices in Sweden for zones SE1, SE2, SE3 and SE4 will be 4.28, 1.88, 8.21, and 8.19 €/MWh respectively. The electricity is mainly generated from wind and hydropower (around 42% each), followed by nuclear (14%), solar (2%) and then bio-energy (0.3%). In addition, the hydrogen supply chain design that leads to a lower overall cost is the decentralised design, with a cost of 1.48 and 1.68 €/kgH 2 in scenarios without and with seasonal variation respectively. The seasonal variation in hydrogen demand increases the cost of hydrogen, regardless of the supply chain design. • Techno-economic study of Hydrogen supply chains coupled with electricity system. • Centralized, decentralised, and hybrid hydrogen production models are designed. • Optimal location, sizing and operational schedules are determined. • Decentralised design offers cost efficiency in both demand scenarios. [ABSTRACT FROM AUTHOR]

As the world's second largest producer of natural gas, Iran relies heavily on natural gas for 70% of its energy needs. There are, however, challenges associated with energy supply in remote regions attached to the gas pipeline network, particularly during the cold season when fuel oil is required for heating and energy provisioning. To address this issue, this research proposes the use of alternative energy sources, such as hydrogen, for non-residential energy consumption, including agriculture and livestock. A key objective of this study is to develop an optimization model for the generation of electricity from hydrogen resources, as well as formulate a robust strategy to assist electricity producers in maximizing their profits. Using this model, producers are able to set prices in accordance with their risk tolerance and present them to the market, thus mitigating price fluctuations. In the targeted sectors, the results indicate that appropriate pricing models can prevent rebound effects. Specifically, the implementation of this model can reduce natural gas consumption by up to 20% and fuel oil usage by 15% during peak seasons. Additionally, the optimal pricing strategy can lead to a 10% reduction in greenhouse gas emissions. The results of this research provide stable and reliable solutions to the energy supply challenges in these regions, thus making the energy market more resilient and environmentally friendly. • Study proposes hydrogen for non-residential energy, aids pricing stability. • Optimize electricity generation from hydrogen; maximize producer profits. • Mitigate price fluctuations with producer-tailored pricing strategies. • Target sectors show pricing models prevent rebound effects. • Stable solutions enhance energy resilience and reduce emissions. [ABSTRACT FROM AUTHOR]

Green hydrogen or renewable hydrogen is promising cause it has no carbon emissions in production process, which is water electrolysis with electricity from renewables. Although grids can ensure stable and enough electricity supply, it is difficult to determine whether the grid electricity for hydrogen production is from renewable sources. This article uses EU rules to identify green hydrogen produced by grid electricity and analyse the production. European grids are classified according to the calculated indexes regarding renewables penetration and electricity carbon emission intensity. The negative correlation between green hydrogen production and electricity prices in different time scales and geographical distribution is concluded. Using imported electricity can improve the potential and diversity of green hydrogen production. Nordic countries and Albania have the most potential of green hydrogen production using grid electricity. The results can be a reference for the electricity market on flexibility supply and the future green hydrogen market development. • Nordic countries and Albania have the most green hydrogen production by grid power. • Other bidding zones with lower electricity prices have more green hydrogen production. • Green hydrogen production is negative correlated with hourly electricity prices. • Using imported electricity can improve the production potential and diversity. • Green hydrogen production by grid power can provide flexibility to electricity market. [ABSTRACT FROM AUTHOR]

The emerging threat of climate change underscores the necessity for increased investments in renewable energy sources and hydrogen energy to enhance the transition towards a more sustainable and resilient future. This work presents a simple Linear Programming (LP) mathematical framework to ensure the optimal portfolio management and participation of an aggregator owning a Polymer Electrolyte Membrane (PEM) electrolyzer and renewable energy assets in the electricity, hydrogen and green certificates markets. The objective aims to maximize the aggregator's overall profits by optimally managing green certificates issuance and diminishing the curtailment of the renewable energy production. The proposed approach is applied to an illustrative case study, while additionally a number of scenarios are considered to account for fluctuations in electricity price and wind power production. Furthermore, the impact of expanding electrolyzer's power capacity from the aggregator's side is also investigated. The results obtained illustrate the impact of the above-mentioned scenarios on the optimal operation and profitability of the aggregator. • Optimal scheduling of a RES-electrolyser aggregator in energy markets. • A novel concept for hourly management of available green certificates. • Wind power generation uncertainty greatly affects aggregator's scheduling decisions. • Aggregator optimally manages green certificates according to hourly prices. • Electrolyser power capacity expansion enhances aggregator's profitability. [ABSTRACT FROM AUTHOR]

With the acceleration of energy storage construction, investors are paying attention to how the costs of storage projects can be recovered. Market rules and incentive policies affect the dissemination of energy storage costs. This article simulates the interactive behavior of multiple agents based on evolutionary game theory, taking into account market factors such as prices, capacity, and incentive policies. It views the dissemination of storage costs as an interactive decision-making problem involving multiple agents' adoption of technology, and analyzes how market factors influence the outcomes of cost dissemination. [ABSTRACT FROM AUTHOR]

With its ability to store and transport energy without releasing greenhouse gases, hydrogen is considered an important driver for the decarbonisation of energy systems. As future hydrogen import prices from global markets are subject to large uncertainties, it is unclear what impact different hydrogen and derivative import prices will have on the future German energy system. To answer that research question, this paper explores the impact of three different import price scenarios for hydrogen and its derivatives on the German energy system in a climate-neutral setting for Europe in 2045 using three different energy system models. The analysis shows that the quantities of electricity generated as well as the installed capacities for electricity generation and electrolysis increase as the hydrogen import price rises. However, the resulting differences between the import price scenarios vary across the models. The results further indicate that domestic German (and European) hydrogen production is often cost-efficient. • Comparison of the energy system models REMod, SCOPE SD and Enertile. • Detailed derivation of three import price scenarios for hydrogen and its derivatives. • Transformation pathways in Germany towards a climate-neutral energy system in 2045. • Increasing capacities for electricity generation and electrolysis in all models as the hydrogen import price rises. • Comparison of effects on storage technologies and electricity imports as well as on the industry and transport sectors. [ABSTRACT FROM AUTHOR]

In this paper, a novel stochastic risk-averse mixed-integer linear programming (MILP) model is developed for optimal electricity procurement of hydrogen fuel stations (HFSs) with responsive hydrogen demands. In the developed model, HFS operator may procure its required electricity through day-ahead (DA) market, bilateral contracts, a contract with withdrawal penalty (CWP) and balancing market. The HFS is committed to inject a pre-specified volume of hydrogen into a natural gas network. In the developed stochastic model, the uncertainties in hydrogen demand and DA market prices are characterized as random variables and modeled as scenarios. The developed model is a two-stage model in which the decisions on transactions with bilateral contracts are the first-stage decision variables and the other decision variables belong to the second stage. The risk of suffering from high procurement costs is modeled using the Conditional Value at Risk (CVaR). The results confirm the efficiency of the proposed model both in terms of reduction in HFS expected cost and risk alleviation. The results show that the transaction through bilateral contracts decreases the expected cost and CVaR respectively by 6% and 3%. The results also indicate that the participation in the balancing market decreases the expected cost and CVaR respectively by 6% and 1.6%. As per results, in scenarios in which DA market prices are lower than CWP prices, HFS operator prefers to pay a penalty fee and withdraw from CWP. The sensitivity of HFS profit to the weight factor of risk metric and the participation factor of responsive demands are assessed. [Display omitted] • A model is developed for electricity procurement of hydrogen fuel stations (HFSs). • The uncertainties in demands and market prices are considered. • HFS procures electricity by day-ahead and balancing market and bilateral contracts. • Impact of different markets and transactions on HFS profit is assessed. • Sensitivity of HFS profit to weight of risk metric is assessed. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy (RE) generation in power systems, RE companies are gradually accepted as regular players in electricity markets. Compared with the conventional way, RE generation promotes economic and environmental protection, but also brings uncertainty to power systems. Hydrogen storage stations (HSSs) can alleviate RE's fluctuating generation. Moreover, the integration of hydrogen and electricity has superiorities to mitigate the spatial imbalance of the RE distribution and achieve higher energy efficiency. To this end, the framework of a local integrated electricity - hydrogen market is designed in this paper. Trading mechanisms among RE generation companies (GenCos), HSSs, power distribution company and other hydrogen-related participants are first introduced, based on which the mathematical models are presented. A two-stage market clearing model is then proposed to determine the optimal trading amount and allot profits based on Shapley value. A simple case shows that by participating in proposed market, the power deviation between offering and available capacity of GenCos decreases by 69.5% and 86.0%, respectively, while the profit of HSSs and GenCos is $ 1.60 × 103 within a week. Therefore, cooperating with the hydrogen market can be a potential way for power systems to integrate high proportion of RE. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) can participate in electricity and carbon market trading in China, which can help stimulate the investment of RE. Thus, we propose a bi-level model for non-cooperative game interactions among power generation companies (GenCos), considering the electricity and carbon coupled markets. The upper-level model considers strategic behaviors driven by maximizing profits for GenCos. The lower-level model includes a two-stage electricity market: the day-ahead market and the real-time market; it implements joint clearing of energy and reserves, and continuous trading with competitive auctions in the carbon market based on the clearing results of the electricity market. Simulations are performed based on the actual data of the power system in a certain region of Guangdong Province in China. The results show that electricity and carbon coupled markets can effectively reduce market costs and carbon emissions, improve the society welfare and RE benefits. The uncertain increase in RE output and higher installed capacity reduce the total revenue of the RE in the coupled markets. The initial carbon quota decomposition optimization decision of the coal-fired GenCos and gradual reduction in the initial carbon quota will increase the revenue of the RE GenCos. • A two-stage electricity market considering joint of energy and reserve is proposed. • A competitive auction model is used to depict the clearance of the carbon market. • A bi-level optimal decision model based on non-cooperative game is established. [ABSTRACT FROM AUTHOR]

Nowadays, charging stations, hydrogen refueling stations, and natural gas refueling stations are being integrated into hybrid energy refueling stations (HERSs) to save investment costs and offer diverse refueling services altogether. This research proposes a risk-averse energy dispatch strategy for HERSs, incorporating bounded user rationality and operational uncertainties. A boundedly rational mixed user equilibrium model is proposed to capture the mobility of heterogeneous traffic flow, including electric vehicles (EVs), hydrogen fuel cell vehicles (HFCVs), and natural gas vehicles (NGVs), each with distinct refueling demands characterized by bounded decision-making rationality. A grid-connected HERS model integrated with carbon capture and storage and on-site renewable energy sources is designed to provide sustainable refueling services to EVs, HFCVs, and NGVs. Furthermore, an HERS operator aggregates multiple HERSs to participate in the day-ahead and real-time electricity markets. The optimal equipment operation and energy bidding strategies for the HERS operator are solved using a risk-averse approach based on conditional value-at-risk, addressing operational uncertainties from heterogeneous refueling demand, electricity markets, and renewable generation. Numerical results have validated the effectiveness of the proposed method in promoting net-zero emissions, securing economic benefits, and mitigating risks associated with operational uncertainties. • Sustainable HERSs with on-site RESs and CCS are designed for EVs, HFCVs and NGVs. • Mixed vehicle mobility is captured by a boundedly rational mixed user equilibrium. • HERSs are aggregated to participate in energy transactions in electricity markets. • Uncertainties from user demand, electricity prices and RESs are addressed by CVaR. [ABSTRACT FROM AUTHOR]

The annual compliance cycle of the carbon trading system gives generation companies (GenCos) the flexibility to buy allowances at preferred times rather than the same time that emissions happen. How to estimate the actual emission cost under fluctuating carbon prices and make better generation decisions become challenges for GenCos. A synergistic generation and carbon trading optimization method is proposed in this paper with a non-speculative dynamic (NSD) carbon trading strategy. The NSD strategy utilizes stochastic dynamic programming to make optimal carbon trading decisions and to provide accurate emission cost estimations for GenCos to make generation decisions. To control risks, the NSD strategy forbids intentional speculations in the carbon market but allows both buying and selling of allowances according to the yearly total emissions estimated by the generation strategy. A risk control scheme by adjusting the allowance-emission balancing period is proposed so that GenCos can smoothly shift between the traditional daily-balanced carbon trading strategy and the NSD strategy. Properties of the NSD strategy are analyzed and proved theoretically under some sufficient conditions, which makes the strategy interpretable and trustworthy. Numerical simulations verify that the proposed method can help GenCos lower their emission costs and improve their overall profits in a year. • The annual compliance cycle of the carbon trading system is considered in the synergistic daily generation and carbon trading decisions of generation companies. • Stochastic dynamic programming is used to optimize daily carbon trading transactions and estimate the actual emission cost in a year. • Risks are controlled by setting the allowance-emission balancing periods. [ABSTRACT FROM AUTHOR]

Managing dispersed generation via virtual power plants (VPPs) is crucial for maximizing profits in electricity markets. This paper presents a model aimed at maximizing VPP profit through participation in the energy market. The proposed model addresses grid and security constraints of units using deterministic programming, formulated as an equilibrium-constrained, two-level mathematical optimization model. The first level focuses on maximizing VPP profit, while the second optimizes social welfare. Applying duality theory transforms this two-level model into a mixed-integer linear programming model, further refined using Karush–Kuhn–Tucker (KKT) optimality conditions. Given the inherent conflict in these objectives, a novel algorithm employing water flow dynamics is proposed for solving the model. To enhance method performance, the Pareto criterion and fuzzy decision-making are incorporated. Model tests are conducted on a standard 24-bus IEEE grid, demonstrating its efficiency. For the single-objective problem without line congestion, the solving time was 12 s. Introducing line congestion increased the profit by 13.4 %, from $40,413.21 to $45,837.32. In the two-objective problem without congestion, the profit ranged between $36,928.72 and $42,813.28, and emissions ranged from 275.21 to 2,916.32 pounds. With congestion, the profit range increased by a maximum of 8.7 %, and emissions were reduced by up to 4.6 %. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic strategic bidding approach for a multi-energy microgrid (MEMG) to optimize its participation across electricity, thermal energy, and hydrogen markets. A MEMG powered entirely by renewable energy and integrating these three energy forms is designed using advanced energy conversion and storage technologies. A bi-level model is developed: in the upper level, the MEMG's bidding strategies are optimized to maximize profits under operational constraints and market demands; in the lower level, detailed pricing mechanisms for each energy market are modeled, incorporating physical constraints and market competition. To address uncertainties in renewable energy generation, a chance-constrained approach is employed to mitigate potential market penalties. Moreover, a novel cost estimation method enables the MEMG to effectively price energy during trading. The bi-level problem is transformed into a tractable mixed-integer linear programming (MILP) problem using the Karush–Kuhn–Tucker conditions and linearization techniques. Numerical results show that the MEMG efficiently participates in multiple energy markets, reducing renewable energy curtailment and adjusting its trading strategies based on market conditions, thereby improving overall economic benefits. • An energy trading model for multi-energy microgrids (MEMGs) is developed. • The MEMG participates in the electricity, thermal energy, and hydrogen markets. • A stochastic bi-level approach is proposed to provide strategic bidding. • An energy cost estimation method is proposed to guide energy pricing for the MEMG. • The KKT conditions and linearization methods are used to compute effective solutions. [ABSTRACT FROM AUTHOR]

As the presence of renewable energy sources in the energy and derivatives markets grows, the need to develop comprehensive mathematical frameworks to study their dominant position in the market becomes greater. This work provides a bi-level mathematical model to derive the optimal trading strategies for a strategic renewable energy aggregator, exerting market power in a joint auction-based electricity and green certificates market. The upper-level problem aims at maximizing strategic player's expected profits, while the two lower-level problems represent the sequential day-ahead electricity and green certificates market clearing mechanism, respectively. The bi-level model is initially reformulated into a mathematical program with equilibrium constraints (MPEC), employing the Karush-Kuhn-Tucker conditions and strong duality theory, and is further recast into a mixed integer linear program (MILP). The proposed approach is first applied to a Pennsylvania-New Jersey-Maryland (PJM) 5-bus transmission constrained power network and then to a modified IEEE 24-bus system to illustrate its computational efficiency and applicability in a real-life case study. Numerical simulations determine electricity and green certificates clearing prices and optimal trading plan for the strategic renewable aggregator in order to maximize its expected profits, under plausible power network congestion. [ABSTRACT FROM AUTHOR]

Fundamental optimization models can determine normative cost-optimal systems following theoretical assumptions of markets with perfect competition and perfect foresight. However, in real markets, deviations from ideal assumptions need to be considered for designing efficient policies: they may lead to higher costs than theoretically expected and contribute to the so-called "efficiency gap". Agent-based simulation models can consider market inefficiencies but are not ideally suited for determining normative systems. This work aims to reduce the efficiency gap between theoretically cost-optimal system configurations and their implementations in non-perfect markets by coupling a fundamental electricity market model and an agent-based model. A fully automated iterative coupling is demonstrated, and its convergence behavior is investigated for two coupling parameters. Fast convergence is observed when integrating peak capacity usage from the previous agent-based simulation into the fundamental model. However, the efficiency gap is reduced only to limited extent as no information on hourly dispatch is transferred between the models. Conversely, when using hourly storage dispatch as coupling parameter, it takes longer to achieve convergence. In return, the efficiency gap is reduced to larger extent. The proposed iterative coupling thus allows the identification of realizable cost-optimal systems, which means that they are achievable under non-perfect market conditions. • Integration of market inefficiencies into the optimal planning of future electricity systems. • Iterative coupling between a fundamental and an agent-based electricity market model. • Comparison of two coupling parameters: peak capacity usage and hourly storage dispatch. • Reduction of the efficiency gap by up to 40.8%. • Identification of realizable cost-optimal electricity system configurations. [ABSTRACT FROM AUTHOR]

We study the electricity retailer's contract design problem when an electricity battery provider enters the market to provide storage services to end-users. For a social planner or perfectly informed retailer, batteries have no impact on retailer pricing or end-user consumption. For rent-seeker retailers with imperfect information, we model the screening problem in a principal–agent setting in which the monopolist offers a menu of consumption bundles and transfers. The incentive compatibility restrictions take into account the agents' option to access a substitution technology in the aftermarket. In the absence of both arbitrage opportunities and dis-economies of scale, agents do not rely on the battery technology in either the first-best or the second-best allocations. Also, even without dis-economies of scale, substitution technology reduces the retailer's surplus under the second best. Furthermore, we show that agents' surplus and social welfare may increase or decrease with batteries. We empirically assess the impact of batteries on California PG&E using its data from household hourly electricity consumption. We develop a structural model of household utility by estimating monthly aggregate elasticities from tiered-rate households, and substitution elasticities from time-of-use-rate households. We use unsupervised machine learning to cluster consumers according to their consumption patterns and obtain rich type-heterogeneity. • We study the electricity retailer's contract design problem with new battery services. • For perfectly informed retailers, batteries don't affect pricing or consumption. • Rent-seeker retailers face a screening problem in a principal-agent setting. • Monopolist offers a menu of consumption bundles and transfers considering substitution tech. • Agents don't rely on battery tech in both first-best and second-best allocations. • Substitution tech reduces retailer's surplus under the second best, even without scale diseconomies. • Agents' surplus and social welfare may vary with the introduction of batteries. • We empirically assess batteries' impact on California PG&E using household consumption data. • We develop a structural model of household utility using monthly aggregate elasticities. • We use unsupervised machine learning to cluster consumers by their consumption patterns. [ABSTRACT FROM AUTHOR]

• Three game models for wind-related storage investments in direct ownership, cooperative, and competitive modes are proposed. • Storage investment in direct ownership mode maximizes profitability by boosting market prices and reducing wind spillage. • Storage investment in cooperative mode offers a suboptimal solution for the profitability of wind farms. • Storage investment in competitive mode can suppress market prices for wind power and reduce the profitability of wind farms. With the increasing share of wind power in the energy sector, many countries start to cut back supporting policies for wind power and shift towards market-oriented schemes, challenging the profitability of wind farms. Energy storage offers a flexible solution to enhance their profitability. This work explores different wind-related storage investment modes, including 1) direct ownership, 2) cooperative, and 3) competitive modes in a market-based environment. For the direct ownership mode, a bilevel single-leader-single–follower Stackelberg game model is proposed, where wind farms invest in and operate storage facilities strategically to maximize their profits in the upper level, while the lower-level problem represents the system operator' s market-clearing process. A cooperative game framework is presented for the cooperative mode, that wind farms and storage investors agree on a profit allocation rule, i.e., Shapley value or Nucleolus to collaborate in investing and bidding as a coalition. The competitive mode is interpreted as a multi-leader-single-follower Stackelberg game, describing an independent investor investing in and operating storage facilities in competition with wind farms. Case studies conducted on a 6-bus and the IEEE 30-bus test systems demonstrate that storage facilities directly invested in by wind farms are the best option for maximizing their profits, resulting in up to an 8.7% increase. The cooperative option provides a suboptimal increase of up to 3.1%, diversifying the costs and risks associated with storage investments. In contrast, the competitive mode can diminish wind farms' profitability, with up to a 30.6% decrease in profits. [ABSTRACT FROM AUTHOR]

Promoting the widespread adoption of multi-complementary low carbon power generation technologies is a fundamental strategy for attaining carbon neutrality within the electricity sector. In the context of electricity market reform, this study develops an agent-based modeling framework integrated simulation with optimization. The model uses agent-based simulation to analyze annual market dynamics and low-carbon technology diffusion, with a two-stage optimization for energy storage and spot market simulation. The research findings indicate: (1) For different power generation technologies, the spot market can establish differentiated average prices, making the market price of carbon-captured coal power higher than that of conventional coal power, which are advantageous in reducing the subsidies required to promote the diffusion of carbon capture technology. (2) Expanding the spot market scale and implementing differentiated capacity compensation mechanisms both promote carbon capture technology diffusion. Nevertheless, with the marginal clearing mechanism, thermal power holds a average price advantage than renewable energy. Failing to control the growth of thermal power capacity will result in increased carbon emissions. (3) After 2030, energy storage's role in balancing supply and demand grows. Storage capacity should align with renewable energy scale and the regional characteristics of wind and solar resources to prevent overbuilding and stranded assets. [Display omitted] • An agent-based modeling framework integrated simulation with optimization was built. • Explored the impact of electricity spot market on the diffusion of low-carbon technologies. • Spot market establishes diverse electricity prices for various power generation technologies. • Inhibiting coal capacity expansion is more crucial than promoting low-carbon technology. • Energy storage capacity should align with regional characteristics and the transition stage. [ABSTRACT FROM AUTHOR]

As the integration of variable renewable energy sources (VREs) into power grids escalates, effectively managing the unpredictability of VRE generation becomes crucial. Electric vehicles, utilized as distributed energy storage, emerge as a potential solution. However, it remains unclear whether electric vehicle aggregators (EVAs), acting as demand dispatch resources, have a favorable impact on the overall electricity market, especially when considering their performance in real-time markets during day-ahead energy allocations. Therefore, this paper proposes a decentralized clearing mechanism (DCM) relying on an multi-attribute evaluation model of EVAs for optimal bidding strategies in the day-ahead market. The EVAs multi-attribute evaluation model employs an integration of the Entropy-Analytic Hierarchy Process, allowing for the simultaneous consideration of real-time performance and day-ahead bidding prices of EVAs. Consequently, the decentralized clearing mechanism supports strategic electricity planning in the day-ahead market by incorporating the actual market performance of EVAs. Furthermore, a real-time market bidding model is developed to balance dynamic mismatches between VRE generation and demand, aiming to maximize profits through optimized real-time commitment and real-time dispatch. System simulations on the IEEE39 bus system demonstrate that the DCM enhances overall profits by 18.69% compared to the centralized clearing mechanism. • Proposed a two-stage bi-level stochastic model for EVAs and VRE generators. • Integrated EVAs' real-time performance into day-ahead market clearing. • Analyzed EVAs and VREs' dynamic bidding strategies. • Analyzed the relationship between EVA dispatchable capacity and VRE penetration. [ABSTRACT FROM AUTHOR]

The fabrication of microgrids to harness renewable resources for local load provision has emerged as a promising concept. Efficient energy management and resource utilization within the electricity market have become crucial tasks for microgrid operation. This article presents a comprehensive model that addresses economic and technical considerations, as well as uncertainties associated with load, wind speed, and solar radiation. Specifically, the focus is on optimizing the utilization and energy consumption of controllable loads within a distributed energy system, taking into account consumer preferences and operational constraints. A two-stage planning problem is formulated to minimize microgrid operation costs and consumer payments, while considering load requirements, restrictions, and utility-imposed constraints. To solve this problem, an enhanced algorithm based on the mountain gazelle optimizer with improved local search operators is proposed, significantly enhancing its search capabilities. The retail electricity market is modeled using real-time pricing (RTP) and incentive-based regulation (IBR) tariffs to capture wholesale price fluctuations and discourage simultaneous consumption. Notably, price forecasting becomes essential in scheduling controllable loads, introducing uncertainties that are addressed by a two-point estimate method. Simulation results demonstrate the effectiveness and robustness of the proposed approach, exhibiting low standard deviation and high resilience. The results highlight the potential of the proposed method in achieving cost-effective and reliable operation of microgrids in the face of uncertainties and dynamic market conditions. [Display omitted] • Framework cuts microgrid & user costs, models preferences, load limits. • Uses RTP, IBR tariffs for price flux, needs forecast. • Paper presents energy scheduling framework, accounts for distributed sources, prices. • MGO algorithm boosts microgrid optimization, factors in economy, tech, uncertainties. [ABSTRACT FROM AUTHOR]

In recent years, distributed energy resources (DERs) have developed greatly, and the total capacity of resources aggregated by virtual power plants (VPPs) has increased. The role VPP plays in the market is changing. It is necessary to explore VPP's bidding strategy in the electricity market based on the impact of VPP on the clearing price. At the same time, the strategy game of different market players will also impact VPP's strategy. The multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm is used to solve the problem of price-maker VPP participation in the day-ahead (DA) market. VPP is required to submit the power and prices of electricity in multi-segments at different times. In the market bidding process, the main market participants are considered as agents. Iterative refinement of each player's strategy is achieved through multi-agent reinforcement learning, with the primary aim of maximizing revenue. For the VPP, the MATD3 algorithm improved the reward by approximately 65 % and increased the convergence speed by 17 % compared to the multi-agent deep deterministic policy gradient and multi-agent proximal policy optimization algorithms. The effects of different influences on VPP's bidding strategy are analyzed, which can provide a decision-making reference for market participants. • The VPP is seen as a price-maker when participating in the day-ahead market. • The TD3 algorithm is introduced for VPP to bid, enhancing bidding adaptability. • Multi-segment and multi-hour bidding strategies are examined. • The impact of various factors on the VPP's bidding strategies is analyzed. [ABSTRACT FROM AUTHOR]

The electricity industry is a cornerstone of modern economies, requiring accurate forecasting to facilitate informed decision-making aligned with sustainable development goals. This study employs a novel, comprehensive system dynamics model comprising four subsystems: consumption, production, carbon emissions, and electricity trading. Our analysis reveals significant trends in Iran's electricity industry. The household sector is projected to become the largest energy consumer, with anticipated reductions in consumer sector subsidies over time. By 2040, total electricity production is forecasted to reach 862,835 million kWh, with renewable sources contributing only 3.3 percent. This trajectory is accompanied by a substantial increase in carbon emissions, reaching 448,383 tons, an alarming 2.6-fold surge compared to 2020. The private sector is anticipated to account for 70 percent of these emissions. Given the findings, prioritizing renewable electricity sources becomes imperative to align with sustainable development goals. Utilizing this integrated model, stakeholders can formulate informed strategies and interventions to navigate the evolving landscape of Iran's electricity sector. • It is expected that the most consuming sector will be the household sector. • CO2 emissions of electricity production in thermal power plants will increase 2.5 times from 2002 to 2040. • In Iran, electricity losses are much higher than its exports, and the ratio of losses to exports will reach 5.42 in 2040. • If the current situation continues, Iran will face a severe shortage of electricity. [ABSTRACT FROM AUTHOR]

The sector integration of the heat and electricity sectors promotes the flexibility of the energy system. This, together with the already almost carbon dioxide-free electricity supply in Finland, facilitates the environmental transition to a carbon-neutral heating system. In this study, pathways to decarbonize the district heating system in Åland (an autonomous region of Finland) are explored. Especially, the roles of different power-to-heat technologies, e.g., heat pumps and direct electric heating, and thermal energy storage are investigated in the decarbonization of the district heating system. The focus is on studying the impact of varying prices in electricity markets on the operation of climate-neutral district heating systems. For this, three scenarios were modeled over the five years of 2018–2022 with varying demand and electricity market prices by using the modeling software PLEXOS. The study showed that electricity prices, but also reserve market prices, have a very significant impact on the operation cost and profit of a district heating system. Overall, electrification has the strong potential to reduce emissions cost-effectively in a district heating system, but it also makes the system vulnerable to electricity price volatility. It can be concluded that thermal energy storage and reserve market participation are crucial for protecting against cost increases and reducing investment risk. • Three district heating scenarios modeled with the modeling software PLEXOS. • Five years of electricity market data used for varying operating conditions. • The proposed electrified heating systems reach price parity in typical price years. • Thermal storage and reserve markets protect against price volatility. • The results strongly support the connection of Åland to the Nordic reserve market. [ABSTRACT FROM AUTHOR]

Recent European Community directives introduce Renewable Energy Communities (REC) and Jointly Acting Renewable Self-Consumers (JARSC). Both entities are constituted by communities of residential and/or non-residential prosumers, located in proximity of renewable generators and Electrical Storage Systems (ESS) owned and managed by the REC/JARSCs. These aggregations of prosumers are aimed at providing environmental and economic benefits by maximizing their global self-consumption. In this frame, it is relevant to introduce a control strategy which considers the whole system represented by the REC/JARSCs and performs optimal management of energy production, storage and consumption. The present paper proposes a Model Predictive Control (MPC) based control design, targeted at the minimization of electricity cost and equivalent CO2 emissions, considering the whole ensemble of loads included in the REC/JARSCs over a 24-h prediction horizon. To exploit the MPC ability of including forecasts in the optimization problem, predictors including Artificial Neural Networks (ANN) are developed for solar irradiance, air temperature, electricity price and carbon intensity. The proposed control performance is evaluated considering a case study located in Milan, Italy, and its advantages with respect to traditional control algorithms are highlighted by comprehensive numerical simulations. Lastly, an economic evaluation of the considered system is presented. • RECs and JARSCs are new forms of self-consumption introduced by EU directives. • RECs/JARSCs operation approached as an optimization problem by an MPC Controller. • ANN-based predictors for PV production, electricity price and carbon intensity. • Evaluation of economic revenues and CO2 emission reduction in different Scenarios. [ABSTRACT FROM AUTHOR]

The Brazilian electric energy compensation system (EECS) states that 100% of the energy generated and inserted into the grid should be returned to prosumers as credits to their energy bill. Ongoing regulatory changes propose that compensation should only be provided for the energy cost (43%). From this perspective, with awareness of the complementarity between wind and solar sources, energy storage systems (ESS) applied to hybrid distributed generation (DG) can become attractive. This study aimed to assess the economic feasibility of hybrid DG power plants with battery banks. Stochastic analyses were carried out by varying nine of the variables (nominal power, solar radiation, wind speed, electricity demand, energy tariffs, discount rate, battery bank investment, and solar and wind installation costs) in three types of hybrid power plants (micro, mini, and small). All scenarios presented a high probability of viability. The main conclusions are as follows. The complementarity of the sources yields benefits to the system: batteries have a high investment cost, which require incentives for their insertion in the DG as a decrease in battery cost or a specific subsidy. The regulatory framework should consider the possibility of payout for the benefits provided by the use of an ESS for DG, i.e., energy storage is directly related to the smart grid concept. [ABSTRACT FROM AUTHOR]

• Optimal investment decisions in integrated electricity and gas market. • Incorporation of carbon emission trading into generation investment problem. • Emission trading alleviates the negative effects of congestion on new investments. • Emission trading and optimal planning create profit opportunities for producers. • Higher carbon prices mitigate the impact of increased gas prices on investments. Addressing global warming necessitates carbon emissions reduction and renewable energy integration within the energy sector. Gas-Fired Power Plants (GFPP) are appealing to investors due to their low emissions and operational flexibility, which are considered necessary characteristics within low-carbon power systems with increasing renewable energy uncertainty. Investing in GFPPs presents intricate challenges due to the increasingly interdependent electricity and natural gas markets, especially in the light of a low-carbon economy. This work addresses these challenges for a strategic agent by proposing a bi-level optimization framework. The upper-level model derives the optimal electricity portfolio management regarding new investments and strategic biddings, while in the lower-level model, the electricity and gas markets are cleared sequentially under a Carbon Emission Trading Scheme (CETS). Case studies on a Pennsylvania-New Jersey- Maryland (PJM) 5-bus power system and an IEEE 24-bus test system demonstrate the applicability and efficacy of the proposed model in capturing the impact of a transitional integrated market framework on GFPPs investments. Also, introducing stochasticity to the model provides a better insight into the contrasting effects of emission allowance trading and gas prices on investment and bidding strategies. [ABSTRACT FROM AUTHOR]

The rapid development of renewable energy resources poses challenges to the economic and reliable operation of energy systems, necessitating the risk management by utilizing various flexible resources and optimization techniques. However, when there exist multiple risk factors faced by decision maker, it might be difficult to clearly control the risks of intermittent renewables. To resolve this issue, a stochastic dominance-constrained operation strategy leveraging flexible resources is developed for an industrial integrated energy system (IIES), and a risk factor-oriented benchmark selection method is proposed to manage the risks of intermittent renewables given the full profit distribution, which makes the proposed strategy superior to the stochastic strategies using certain risk measures. Firstly, the theory and mathematics of stochastic dominance are systematically introduced, and a stochastic dominance-constrained model is then developed for an IIES in electricity markets. Next, a risk factor-oriented benchmark distribution selection method is proposed by solving the stochastic model without considering a specific risk factor, based on which a stochastic dominance-constrained operation strategy for IIES leveraging flexible resources is obtained. Finally, case studies are conducted by using realistic data in the electricity markets of the United States. It is shown that the intermittent renewables decreased the profits by more than 7 % in half of the scenarios, while the proposed risk-factor-oriented stochastic dominance-constrained approach is shown to effectively manage this type of risk in all scenarios by employing flexible financial and physical resources. If the financial resource in trading account is increased by 33.33 %, the fixed investment cost of installing storage could be decreased by 92.59 %. • A stochastic dominance-constrained optimization model is established for IIES. • An IIES operation strategy with renewables and convergence bids is developed. • Risk factor-oriented benchmark distribution is proposed to manage risks of renewables. • Feasibility is ensured by using additional physical and financial flexible resources. [ABSTRACT FROM AUTHOR]

Electricity deregulation has intensified competition in the clean energy sectors, notably within cascaded hydropower systems. Due to the natural interdependencies among hydropower stations, upstream stations with self-interested motivations can significantly influence downstream operations, creating asymmetrical competition and considerable uncertainty for downstream stations' bidding in the electricity market. To address this issue, this paper, from the perspective of the downstream stations, presents a bilevel model to estimate the market strategies of upstream competitors using inverse optimization and non-private historical data, with a detailed consideration of their hydraulic connections. In the bilevel structure, the upper-level model aims to estimate key parameters of the rivals' generation function, while the lower-level model simulates their bidding behavior using parameters provided by the upper-level model. Engineering experience is incorporated to streamline the decision variables of the upper-level model into only four. To solve this non-convex model, an Enhanced Micro Particle Swarm Optimization (EMPSO) algorithm is proposed, which employs a tabu table-based reinitialization strategy to ensure diversity within the small population and introduces mutation operations to enhance exploration capability. Applications in the Lancang River basin in China demonstrate the model's precision, efficiency, and stability. Specifically, the estimated errors for the rival's generation and power discharge are all within 1%, while the estimated errors for all of the four decision variables are all within 2%. Additionally, it is found that a strong correlation between the objective function and parameter values. The algorithm maintains robust search capability throughout the entire process. Finally, statistical tests confirm the significant superiority of EMPSO over conventional methods. [Display omitted] • A novel inverse bilevel model to solve asymmetric electricity markets competition. • An experience-based model simplification and parameter range determination method. • An enhanced mPSO with a tabu table-based reinitialization and a mutation strategy. • Case study shows that the model and methods are efficient, accurate, and robust. [ABSTRACT FROM AUTHOR]

Backboned by energy-intensive Internet data centers (IDCs), cloud systems have been playing important roles in both modern information and energy systems. This paper proposes a comprehensive portfolio optimization framework to provide operational decision-making support to a cloud service provider managing networked IDCs in trading cloud and energy resources in a cloud market, multiple regional wholesale electricity markets, and multiple local electricity markets. The portfolio management logics are represented by 3 interrelated optimization models; a computational linearization approach is developed, which transforms the 3 models into a single mathematical programming problem with equilibrium constraints based on Karush–Kuhn–Tucker conditions. Extensive simulations and comparison studies are conducted to demonstrate that the proposed framework can effectively optimize the operation of networked IDCs in the combined market environment. • Propose a holistic cloud and electricity portfolio optimization framework for cloud service providers. • Develop a computational efficient numerical approach for cloud and electricity portfolio optimization. • Investigate the impact of cloud market dynamics on cloud service providers' bidding in multiple electricity markets. [ABSTRACT FROM AUTHOR]

Price-makers in the generation sector can utilize offer data published by independent system operators (ISOs) for strategic bidding. Many research uses system-wide offer information for strategic bidding because individual offers are usually published with masked identifications. However, these methods are not applicable in markets with transmission congestion because the nodal information is not used. A research gap remains in using system-wide offer information and accessible nodal information for strategic bidding in markets with congestion. System-wide aggregated supply curves can be formed just with anonymous offers, and locational marginal price (LMP) is accessible nodal data containing the congestion information. This paper proposes a framework for price-makers to bid strategically by predicting LMPs with aggregated supply curves. A feature extraction method is proposed to make aggregated supply curves applicable for LMP prediction, and the maximal information coefficient is developed for feature selection. A convolutional neural network is combined with a long short-term memory network to model the impact of aggregated supply curves on LMPs. In this framework, price-makers can investigate the impact of their strategies on aggregated supply curves, predict LMPs with aggregated supply curves, and make the optimal bidding strategies. Numerical results based on the PJM-5 bus system and real data from the Midcontinent Independent System Operator validate the effectiveness of the proposed framework. • We propose a method to use aggregated supply curves for LMP prediction. • We design a CNN-LSTM for price-makers to bid strategically by LMP prediction. • Experiments based on the PJM 5-bus system and MISO validate the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, the role of a Battery Energy Storage System (BESS) in the United Kingdom (UK) electricity market is investigated. Such device is selected since research works related to the topic demonstrate that BESSs help facing challenges caused by renewable energy sources increasing penetration in the power systems' field. Indeed, in the next future, BESSs will assume a relevant role thanks to their fast response time. In the present paper, an optimization model is implemented to encode the UK market rules and output the optimal set of offers on day-ahead and intraday markets, dynamic frequency response services and imbalance settlement. The market rules are formalised in a mathematical model considering market temporal sequencing and BESS technical limitations. The tool also models the state of energy management, necessary since BESSs are energy limited assets. Objective function and constraints are linearized to define a Mixed Integer Linear Programming problem to reduce the computational burden and to integrate the model in the Energy Management System previously developed at University of Genoa. Specific tests are performed including and excluding ancillary services to evaluate the related advantages. The obtained outcomes reveal that ancillary services are the most convenient options within all the implemented scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we study optimal market operations and grid effects of office building from an energy community perspective. Commercial buildings such as offices can include many stakeholders and new flexible assets that can offer suitable use cases to form energy communities or enter energy and reserve markets. The office building we focus in this study has a real-world counterpart with historical measurement data and involves three stakeholders and their assets. The stakeholders are the owner, the tenant, and the operator of flexible assets. These assets are the battery energy storage system (BESS) and the electric vehicles (EV) charging system (EVCS) that both can operate in response to energy price fluctuations and in a reserve market. Different cases are studied where either the owner or the tenant is paying the EV charging, and where the flexibility of the EV charging is either active or not. We show that, when the owner pays for the energy of the EVCS and the tenant is using it, there is a beneficial case to form a coalition between them and share the benefits. Operative cost-benefit calculations are conducted where the community benefits are shared according to the Shapley value and the power flows at the building's connection point are simulated based on the market operations. The sensitivity of the flexibility and the maximum power of EVCS to the results are studied. Harnessing benefits from the markets shows significant effects on the power flows at the connection point. • With multiple stakeholders, flexible assets and varying energy price can initiate a community use case. • Having only fixed energy price is not giving coalitional benefits. • Having excess energy to share or sell is not necessary to form an energy community. • More flexibility and electric vehicle charging power can increase cost savings and coalitional benefits. • Spot price and reserve market operations can have a high impact at the connection point. [ABSTRACT FROM AUTHOR]

Decarbonizing fossil fuel-dependent district heating systems is essential for achieving carbon neutrality, particularly in cold climates. In Finland, district heating operators are concentrating on electrifying these systems. However, the 2022 energy crisis in Europe has highlighted concerns about heat production costs and the security of heat supply with this approach. This study examines the economic feasibility and risks associated with electrified district heating systems and the early decommissioning of thermal power plants in the interconnected district heating systems of Helsinki, Espoo, and Vantaa. The case study is simulated and optimized to find the least-cost solution while meeting heat demand for various 2025 scenarios, assuming high energy market prices as in 2022 and more normal circumstances. Simulation results indicate that shutting down fossil fuel-based combined heat and power plants in Helsinki and Espoo would create a shortfall in base-load heat production, increasing dependency on heat imported from Vantaa. Both cities are expected to employ more cost-competitive biomass boilers to mitigate the reduction in coal-based heat production, which would decrease operational costs but also reduce revenue from electricity sales due to reduced combined heat and power capacity. Consequently, Vantaa is likely to benefit from its substantial storage and waste and biomass combined heat and power capacity, enabling efficient heat production at reduced costs. Across both scenarios, the analysis demonstrates a significant decrease in emissions and less reliance on imported fuels, highlighting the potential benefits of electrified district heating systems even amidst high electricity market prices. [Display omitted] • Fluctuating electricity prices raise concerns about electrified district heating. • The feasibility of interconnected electrified district heating in the Helsinki area. • 2025 scenarios with the planned decommissioning of thermal plants. • Helsinki and Espoo cities become importers of heat and reliant on biomass. • Electrified DH is competitive even with extreme energy prices. [ABSTRACT FROM AUTHOR]

• The trading rules for cross-border electricity markets are designed. • A multilateral alliance game model based on non-cooperative game is built. • The influence of each variable parameter on the Nash equilibrium point was analyzed. Cross-border electricity trade can promote the sustainable development of regional multi-country economies by alleviating the uneven distribution of global energy resources. Hence, designing a reasonable and effective mechanism contributes toward the development of cross-border electricity market. This study established a non-cooperative game model for cross-border electricity trading. Central to our approach is the analysis of multi-participants trading modes influenced by cross-border trading centers. The model distinctively incorporates trading rules and service fees, providing a realistic simulation framework. The results reveal a direct correlation between strategic scope and the dependency of trading strategies on electricity pricing set by trading centers. This research advances the theoretical understanding of game theory application in electricity markets and offers practical insights for policymakers and market operators, particularly in strategizing for price setting and managing cross-border transactions. [ABSTRACT FROM AUTHOR]

This study proposes a novel framework for smart homes to optimize energy consumption and production, leading to reduced costs and a more reliable grid. The framework schedules the use of controllable appliances and renewable energy sources while considering uncertainties in production, real-time market prices, and uncontrollable household loads. By incorporating both incremental and real-time pricing models, the system discourages excessive consumption during peak hours. The core innovation lies in a two-stage scheduling approach implemented using GAMS software. This method minimizes the expected total cost while accounting for limitations on controllable loads, power supply, production resources, battery performance, and overall home energy balance. Additionally, the framework leverages the previous day's bilateral contract and allows residents to adjust desired lighting levels based on current market fluctuations. Simulations demonstrate the program's effectiveness in reducing both net energy costs and peak load on the electricity grid. [ABSTRACT FROM AUTHOR]

This paper addresses the conundrum of optimizing electricity consumption patterns in response to fluctuations in demand and price, a task managed by both load aggregators and the distribution system operator (DSO). The conventional approaches in the literature to integrate demand response (DR) into optimal power flow (OPF) problems typically overlook the price responsiveness of consumers or simplify power flow equations to account for price-elastic demand. In this paper, we strive to close this gap by introducing a bi-level primal-dual optimization framework that incorporates aggregators' objectives into the market-clearing process while preserving the precision of the OPF equations. At the upper level, the model seeks to minimize both the total payment and peak load. The lower level, structured as a second-order conic (SOC) problem, aims to reduce overall generation costs subject to constraints of the second-order conic (SOC) branch flow model (BFM). The two levels interact through the optimal demand response and distribution locational marginal prices (DLMPs). The principles of convex duality principles together with the strong duality constraint are leveraged to transform the market-clearing problem into a single primal-dual problem. We also circumvent the non-linearity that stems from the DR payment term by incorporating discretizing loads and the big- M method, thus converting the problem into a mixed-integer SOC (MI-SOC) formulation. The merits of the proposed MI-SOC framework are validated through case studies conducted on the IEEE 33-bus test system, showcasing its potential to enforce price-elasticity demand response in distribution system's electricity markets. [ABSTRACT FROM AUTHOR]

To facilitate the coupling of electricity and carbon markets, a two-layer bidding model considering a multi-stage offer‑carbon joint incentive clearing mechanism is proposed. The upper layer models the bidding strategies of renewable energy units and thermal power generating units, and the lower layer is the market clearing layer. A multi-stage offer‑carbon joint incentive clearing mechanism is proposed. In the pre-clearing stage, the environmental benefit factor of the carbon market is introduced to adjust the bidding strategy of the units, the carbon emission intensity enters the market in the form of price, the environmental benefit of the generating units is fully considered, and the carbon quota allocation model of units is established; in the second stage, carbon trading cost is added based on the pre-clearing model to further optimize the unit output; in the third stage, the clearing model with the minimum cost of electricity energy is established for tracking the clearing price. Under this mechanism, the carbon trading cost is traded on the market and unit offers are combined with carbon element to participate in market trading. Three bidding models with different clearing mechanisms are set up, and the results of the arithmetic example show that the proposed model can realize the balance between the economic and low-carbon goals of market clearing, and can achieve the maximum carbon emission reduction based on no increase in the total cost of electric-carbon coupling. As the share of renewable energy increases, the clearing price will decrease. A reasonable offer from renewable energy sources can avoid their interests being jeopardized. Concurrently, increasing the initial carbon price can effectively guide the electricity market to develop in a cleaner direction. • Pre-clearing phase is introduced to adjust bidding strategies. • A multi-stage offer-carbon joint incentive clearing mechanism is proposed. • A two-layer bidding model considering a multi-stage offer-carbon joint incentive clearing mechanism. • The market equilibrium problem is modeled as a two-layer problem, and the model is solved by Aquila Optimizer. [ABSTRACT FROM AUTHOR]

With the promotion of distributed energy trading and the transformation of power system operation mode, prosumers in the carbon and electricity markets face new opportunities and challenges. This paper proposes a peer-to-peer transaction platform to promote renewable energy consumption and carbon emission reduction, including multiple types of loads, coal-fired units, and microgrid prosumers. The carbon-emitting members of power generation and demand sides in the electricity market are introduced into the carbon market, and their carbon cost model is established. To guide consumption behavior of demand side, the user utility model based on electricity consumption is built to reduce carbon emissions. With the goal of optimizing the generation cost, user utility, and network fees, the distributed optimal scheduling model is established to recover the network fees. Finally, the alternating direction method of multipliers is used to solve this problem and protect transaction privacy. The results indicate that introducing carbon trading at the power generation and demand sides can promote the shift of members' production behavior from high-carbon emission industries to low-carbon ones. Additionally, an appropriate network fee mechanism and unit fee can guide market members to adjust their trading behavior adaptively and help the power grid recover costs. [ABSTRACT FROM AUTHOR]

This paper presents a tool that allows to perform an ex-ante estimation of the profitability to participate to the electric energy and service markets for a microgrid or a polygeneration power plant. The tool is the evolution of an Energy Management System (EMS) previously developed by the University of Genoa and Renantis Solutions Srl. Constraints are added to the original EMS to model the Ancillary Service Market (ASM) and Day Ahead Market (DAM) mechanisms in the framework of a planning tool that allows to get estimates of the microgrid/plant costs and revenues over a long-time horizon. As the market outcome strongly depends on the Transmission System Operator (TSO) acceptance/rejection of bids and offers, a statistical approach is proposed. The optimization process is split in two steps: the first one optimally dispatches the energy units and proposes DAM offers and ASM bid and offers over a specified time horizon. Then, a Monte Carlo method is launched; at each extraction the TSO acceptance/rejection of each ASM bid and offer is simulated and there is a second step of optimization in which the energy sources are optimally re-dispatched to meet the bidding program and so to avoid imbalance power exchange that would increase the energy cost. The cost/revenue is thus computed at each extraction. The result is the probability density function of the costs/revenues that allows to estimate the profitability of the participation to the electricity markets. One test performed on a real microgrid shows that the computational time needed to complete one iteration related to a time horizon of one month is approximately 20 s. Moreover, the comparison of the outcomes with two independent launches to define the set of bids and offers to be accepted in ASM demonstrates that the statistical analysis allows to avoid misleading conclusions on the costs/revenues. Consequently, the fact that the developed EMS allows to perform a statistical analysis in a reasonable time enables the possibility of comparing multiple scenarios and gives an indication related to the range of cost/revenues in which the microgrid/plant may incur. Finally, the computational efficiency of the proposed approach allows to conclude that the developed EMS is appropriate to perform a long-time horizon (of the order of magnitude of years) analysis and, at the same time, allows to model all the involved components with a high level of details. [ABSTRACT FROM AUTHOR]

Gathering knowledge of supply curves in electricity markets is critical to both energy producers and regulators. Indeed, power producers strategically plan their generation of electricity considering various scenarios to maximize profit, leveraging the characteristics of these curves. For their part, regulators need to forecast the supply curves to monitor the market's performance and identify market distortions. However, the prevailing approaches in the technical literature for analyzing, clustering, and predicting these curves are based on structural assumptions that electricity supply curves do not satisfy in practice, namely, boundedness and smoothness. Furthermore, any attempt to satisfactorily cluster the supply curves observed in a market must take into account the market's specific features. Against this background, this article introduces a hierarchical clustering method based on a novel weighted-distance that is specially tailored to non-bounded and non-smooth supply curves and embeds information on the price distribution of offers, thus overcoming the drawbacks of conventional clustering techniques. Once the clusters have been obtained, a supervised classification procedure is used to characterize them as a function of relevant market variables. Additionally, the proposed distance is used in a learning procedure by which explanatory information is exploited to forecast the supply curves in a day-ahead electricity market. This procedure combines the idea of nearest neighbors with a machine-learning method. The prediction performance of our proposal is extensively evaluated and compared against two nearest-neighbor benchmarks and existing competing methods. To this end, supply curves from the markets of Spain, Pennsylvania-New Jersey-Maryland (PJM), and West Australia are considered. • We study the supply curves of several electricity markets in a five-year period. • We introduce a novel market-based distance to measure the curves' dissimilarity. • Clusters are provided with an insightful characterization of the resulting groups. • A distance-based learning procedure is proposed to forecast the supply curves. [ABSTRACT FROM AUTHOR]

• Mechanisms to encourage clean energy in Spain from 2005 to the present are analyzed. • FiT and FiP mechanisms can be effective in promoting new renewable projects. • Experience suggests that excessive support can jeopardize the entire market. • A transitional retribution scheme has proven to be effective if correctly supervised. This research paper delves into the central mechanisms implemented in Spain to foster the adoption of clean energy projects. The study scrutinizes various mechanisms established from 2005 onwards, evaluating their efficacy in achieving the desired outcome of supporting clean energy project implementation. The aim is to comprehensively assess the associated costs, effectiveness, and attainment of renewable energy objectives stemming from implementing diverse support schemes. This paper explores the effectiveness of feed-in tariffs and feed-in premium mechanisms in promoting the integration of new renewable energy generation into the power system. However, the Spanish experience suggests that exhaustive planning control is imperative to prevent excessive support costs that could jeopardize the entire market and cause a significant rise in final energy costs. The research studies the support schemes and associated market distortion and presents the roadmap from regulated tariffs to a "total market" for wind and solar energy. The findings demonstrate that, under proper supervision and planning, a transitional retribution scheme effectively promotes renewable energy plant integration. Finally, the analysis examines the need to regulate the excess benefits that non-emitting power sources receive in a marginal price market, as this can lead to market imbalances and a substantial increase in final costs. These plans serve as a crucial tool in achieving the decarbonization objectives of the European Power System. [ABSTRACT FROM AUTHOR]

The state-of-the-art centralized computing framework applied to the optimal market dispatch of energy storage systems (ESS) aggregates data from local ESS units for training on the cloud server due to the limited computing resources on edge. However, this approach poses several challenges, including the lack of joint optimization for multiple ESS units, susceptibility to single point of failure and attacks, and inadequate data privacy protection for ESS owners. This study proposes an adaptive personalized federated reinforcement learning (FRL) for multiple-ESS optimal dispatch in various electricity markets with electric vehicle and renewable energy, achieving both the joint optimization of multiple ESSs and avoiding the degraded performance of FRL's local model. Under an adaptively ESS-related differential privacy protection, local devices and the cloud server are specialized to form multiagent deep reinforcement learning (DRL) model for bidding energy, regulation, and third-party services and update the global models, respectively. Given the adaptability of personalization layer to different agents and clients, an adaptive personalization method is developed by calculating the number of personalization layers with the relative loss of each agent and client in the iteration process. The case study shows that the adaptive personalized FRL outperforms conventional FRL, DRL and optimization algorithms. [Display omitted] • FRL-based algorithm for multiple-ESS optimal market dispatch. • Multiagent DRL model for bidding energy, regulation, and third-party services. • Adaptive personalization layers with the relative loss of each agent and client. • Adaptively ESS-related differential privacy method. [ABSTRACT FROM AUTHOR]

Global decarbonisation is driving investments in intermittent renewable generation, with most plant entering the market by way of a Power Purchase Agreement. In Australia's National Electricity Market, a surprising number of renewable generators have entered on a merchant basis. To maintain tractable revenues some minimum level of hedging is required, but in an energy-only electricity market with a very high market price cap, merchant intermittent generators require some level of associated firming capacity to ensure spot price exposures can be adequately managed. This article uses unit commitment, battery arbitrage and stochastic discounted cashflow valuation models to compare an Open Cycle Gas Turbine and a battery as firming options for a hypothetical wind farm in the South Australian region of the NEM. Using historic market data from 2010–2020, we find OCGT firming capacity helps the wind farm to generate consistent net cash flows, allowing it to withstand the highs and lows of spot market prices whilst covering required contract-for-difference payments associated with hedging. When battery firming is deployed, the wind farm/ battery portfolio generated higher cash flows at specific times, reflecting participation in price arbitrage under low spot price scenarios. However, battery performance was constrained by costly storage capacity. [ABSTRACT FROM AUTHOR]

In September 2020, China has made a commitment to achieving "peak carbon dioxide emissions" before 2030 and "carbon neutrality" before 2060 at the General Assembly of the United Nations. Then, at the Climate Ambition Summit, China pointed out the important responsibility and mission of the power industry to serve "peak carbon dioxide emissions and carbon neutrality". The key issues such as what kind of electric market to build and what kind of main energy will be chosen to build the power system were proposed. For the new power system, there will be new changes in the links of "source, network, load, storage". However, the reform of the electricity market cannot be ignored, either. How to determine the type of electricity sales company and how to develop a suitable electricity sales strategy is one of the important studies that need to be explored at present. Therefore, the main body of electricity sales companies under new power system reform in China are analyzed in our studies, Then, we reviewed the current situation of electricity market reform at home and abroad. Finally, the thought on electricity market reform is presented. [ABSTRACT FROM AUTHOR]

Promoting a hydro-dominated electricity market (HEM) is greatly challenging. This study analyzes impacts and challenges of electricity market on hydro-dominated power system operations. The analysis indicates that the HEM exhibits unique difficulties including spatiotemporal hydraulic coupling of cascaded hydropower plants, runoff uncertainties, nonlinear operation constraints, coordination of multiple time scales, as well as transprovincial and transregional power transmission. Two kinds of suggestions are presented to overcome these difficulties for different market objects. The hydropower enterprises are suggested to improve generation prediction level, reconstruct market-based operation rules, implement multiscale nested operations and bidding, strengthen collaborative bidding of different stakeholders, and analyze market demands and competitors. The goal is to improve their competitiveness. The market operators should make efforts to develop accuracy and efficiency of clearing models and predetermine feasible hydropower bidding intervals to ensure power balance and prevent unreasonable spillage. Moreover, a practical and convenient electricity trading platforms is required. It is implied that there is strong necessity to explore characteristic market theories and application technologies combined with hydropower characteristics and actual dispatching needs. • Promoting a hydro-dominated electricity market (HEM) is challenging for China. • The HEM involves complex hydraulic coupling and nonlinear operation constraints. • The impacts and challenges of electricity market on HPSs are described in detail. • Plants should improve the level of prediction, operation, and bidding in the HEM. • Market operators can make efforts to develop clearing models and trading platforms. [ABSTRACT FROM AUTHOR]

• Comparison of day-ahead deterministic forecasts of solar irradiation and of production of a large-scale PV plant. • Use of Numerical Weather Predictions, post-processing methods and benchmark models. • Optimization of the operation of an energy storage system associated to a PV farm using the solar forecasts and a receding horizon approach. • Evaluation of the economic gain brought by the forecasts in order to increase the revenue offered by the energy arbitrage of the Australian electricity market. • Highlight of relationships between quality and economic value of solar forecasts. During the last decade, numerous solar forecasting tools have been developed to predict the energy generation of photovoltaic (PV) farms. The quality of solar forecasts is assessed by comparing predictions with measured solar data. However, this methodology does not consider the added value of the forecasts for their applications. As a consequence, what value could be given to the improvement of forecasts considering this evaluation framework? To answer this question, this work compares the value of different operational solar forecasts for a specific application. The aim is to look for relationships between the economic value and the error metrics defined to evaluate the forecast quality. A new generation of large-scale PV plants integrates ESS. The aim is to add flexibility to the injection of the production into the grid and thus to maximize the profit by taking advantage of the possibilities offered by the electricity market, such as energy arbitrage. To optimize the operation of these specific ESS, forecasting of the solar production is of paramount importance. The study case considered in this work is a large-scale PV farm of several megawatts associated with Li-ion batteries in the Australian energy market context. For this specific case study, the results show that the metrics used to evaluate the forecast quality based on the mean absolute error (MAE) have an almost linear relationship with the economic gain brought by applying the forecast. More precisely, an improvement of 1% point in MAE results approximately in an increase of 2% points in economical gain. [ABSTRACT FROM AUTHOR]

Electric logistics vehicles (ELVs) have the potential to significantly reduce pollution and carbon emissions, which are considered highly promising for achieving green logistics. However, the challenges posed by time-consuming charging processes, indirect carbon emissions, and fluctuating electricity prices hinder the economic viability of ELVs. To enhance the competitiveness of ELVs compared to internal combustion engine vehicles, this study introduces a coordinated optimization framework that aims to minimize both logistics and electricity costs within an urban logistics system. In terms of logistics, a simultaneous loading and partial charging strategy is proposed to improve the logistics efficiency of ELVs by effectively utilizing the charging time. In terms of electricity, we integrate suburban renewable energy sources with ELVs to reduce indirect carbon emissions resulting from thermal power generation. Additionally, ELVs are aggregated to participate in bidding in the day-ahead and real-time electricity markets through charging scheduling. To address uncertainties related to electricity prices and renewable generation, optimal logistics scheduling and electricity dispatch strategies for ELVs are formulated as a stochastic programming problem. This problem is then solved using a specially designed matheuristic approach. The effectiveness of the proposed coordinated optimization framework is confirmed through case studies on a large-scale urban logistics system. • Logistics efficiency is improved by a simultaneous loading and charging strategy. • Suburban RESs and ELVs are effectively integrated to achieve green logistics. • Uncertainties of energy markets and RESs are addressed by stochastic programming. • The proposed framework can enhance the competitiveness of ELVs compared to ICEVs. [ABSTRACT FROM AUTHOR]

With the increasing uncertainty caused by the complexity of the world's energy environment and the increasing penetration rate of renewable energy, it is significant to estimate the future operation of power markets in advance. Forecasting individual bids in spot electricity markets is a promising new method for achieving so, but it has not been fully studied due to the difficulty of forecasting a bid function. The idealization of existing optimization-based models decreases their practical effects in real markets. Thus, we propose a scalable forecasting framework that incorporates several customized state-of-art machine learning methods according to the characteristics of the bidding data. First, several low-rank approximation algorithms are customized to encode the high-dimensional bidding curves into low-dimensional feature spaces and reconstruct them from the predicted feature space. Second, a transformer-based multidimensional time series prediction algorithm is proposed to predict the bidding feature based on both related factors and historical bidding records. To appropriately evaluate the performances of the forecasting methods, we introduce a dynamic criterion based on the economic implications of bids. The comprehensive framework is tested based on actual market data from the Australian national electricity market, and in the empirical example, the feasibility and effectiveness of the proposed framework are demonstrated. • A scalable framework for forecasting individual bids in real electricity markets. • A bidding encoding method processes the bidding curves in a machine learning way. • A transformer-based multi-dimensional forecasting model for time-series forecasting. • Performance evaluation based on real data from the Australian electricity market. [ABSTRACT FROM AUTHOR]