Search Results (24)

Virtual power plants (VPPs) enable the efficient participation of distributed renewable energy resources in electricity markets by aggregating them. However, the profitability of VPPs is challenged by market volatility and regulatory constraints, such as price caps and imbalance penalties. This study examines the joint impact of varying price cap levels and imbalance penalty structures on the bidding strategies and revenues of VPPs. A stochastic optimization model was developed, where a three-stage scenario tree was utilized to capture the uncertainty in electricity prices and renewable generation output. Simulations were performed under various market conditions using real-world price and generation data from the Korean electricity market. The analysis reveals that higher price cap coefficients lead to greater revenue and more segmented bidding strategies, especially under asymmetric penalty structures. Segment-wise analysis of bid price–quantity pairs shows that over-bidding is preferred under upward-only penalty schemes, while under-bidding is preferred under downward-only ones. Notably, revenue improvement tapers off beyond a price cap coefficient of 0.8, which indicates that there exists an optimal threshold for regulatory design. The findings of this study suggest the need for coordination between price caps and imbalance penalties to maintain market efficiency while supporting renewable energy integration. The proposed framework also offers practical insights for market operators and policymakers seeking to balance profitability, adaptability, and stability in VPP-integrated electricity markets. Full article

The large-scale integration of renewable energy and electric vehicles(EVs) into power distribution systems presents complex operational challenges, particularly in coordinating market mechanisms with grid stability requirements. This study proposes a new dispatching method based on dynamic electricity prices to coordinate the relationship between the market and the physical characteristics of the power grid. The proposed approach introduces a multi-agent transaction model incorporating voltage regulation metrics and network loss considerations into market bidding mechanisms. For EV integration, a differentiated scheduling strategy categorizes vehicles based on usage patterns and charging elasticity. The methodological innovations primarily include an enhanced scheduling algorithm for coordinated optimization of renewable energy and energy storage, and a dynamic coordinated optimization method for EV clusters. Implemented on a modified IEEE test system, the framework demonstrates improved voltage stability through price-guided energy storage dispatch, with coordinated strategies effectively balancing peak demand management and renewable energy utilization. Case studies verify the system’s capability to align economic incentives with technical objectives, where time-of-use pricing dynamically regulates storage operations to enhance reactive power support during critical periods. This research establishes a theoretical linkage between electricity market dynamics and grid security constraints, providing system operators with a holistic tool for managing high-renewable penetration networks. By bridging market participation with operational resilience, this work contributes actionable insights for developing interoperable electricity market architectures in energy transition scenarios. Full article

The increasing adoption of Renewable Energy Sources (RESs), due to international energy policies mainly related to the decarbonization of electricity production, raises several operating issues for power systems, which need “flexibility” in order to guarantee reliable and secure operation. RESs can be considered examples of Distributed Energy Resources (DERs), which are typically electric power generators connected to distribution networks, including photovoltaic and wind systems, fuel cells, micro-turbines, etc., as well as energy storage systems. In this case, improved operation of power systems can be achieved through coordinated control of groups of DERs by “aggregators”, who also offer a “flexibility service” to power systems that need to be appropriately remunerated according to market rules. The implementation of the aggregator function requires the development of tools to optimally operate, control, and dispatch the DERs to define their overall flexibility as a “market product” in the form of bids. The contribution of the present paper in this field is to propose a new optimization strategy for flexibility bidding to maximize the profit of the aggregator in flexibility markets. The proposed optimal scheduling procedure accounts for important practical and technical aspects related to the DERs’ operation and their flexibility estimation. A case study is also presented and discussed to demonstrate the validity of the method; the results clearly highlight the efficacy of the proposed approach, showing a profit increase of 10% in comparison with the base case without the use of the proposed methodology. It is evident that quantitatively more significant results can be obtained when larger aggregations (more participants) are considered. Full article

The deepening operation of the carbon emission trading market has reshaped the cost–benefit structure of the power generation side. In the process of participating in the market quotation, power suppliers not only need to calculate the conventional power generation cost but also need to coordinate the superimposed impact of carbon quota accounting on operating income, which causes the power suppliers a multi-time-scale decision-making collaborative optimization problem under the interaction of the carbon market, power market, and coal market. This paper focuses on the multi-market-coupling decision optimization problem of thermal power suppliers. It proposes a collaborative bidding decision framework based on a multi-agent deep deterministic policy gradient (MADDPG). Firstly, aiming at the time-scale difference of multi-sided market decision making, a decision-making cycle coordination scheme for the carbon–electricity–coal coupling market is proposed. Secondly, upper and lower optimization models for the bidding decision making of power suppliers are constructed. Then, based on the MADDPG algorithm, the multi-generator bidding scenario is simulated to solve the optimal multi-generator bidding strategy in the carbon–electricity–coal coupling market. Finally, the multi-scenario simulation based on the IEEE-5 node system shows that the model can effectively analyze the differential influence of a multi-market structure on the bidding strategy of power suppliers, verifying the superiority of the algorithm in convergence speed and revenue optimization. Full article

The engagement of emerging market participants in electricity markets exerts dual influences on price formation mechanisms and operational dynamics. To quantify the impacts on locational marginal prices and stakeholders’ economic interests when EV aggregators (EVAs), cloud energy storage operators (CESSOs), and load aggregators (LAs) collectively participate in market competition, this study develops a bi-level game-theoretic framework for market equilibrium analysis. The proposed architecture comprises two interdependent layers: The upper-layer Stackelberg game coordinates strategic interactions among EVA, LA, and CESSO to mitigate bidding uncertainties through cooperative mechanisms. The lower-layer non-cooperative Nash game models competition patterns to determine market equilibria under multi-agent participation. A hybrid solution methodology integrating nonlinear complementarity formulations with genetic algorithm-based optimization was developed. Extensive numerical case studies validate the methodological efficacy, demonstrating improvements in solution optimality and computational efficiency compared to conventional approaches. Full article

With the transformation of the energy structure, the integration of numerous small-scale, widely distributed renewable energy sources into the power grid has introduced operational safety challenges. To enhance the operational competitiveness, the virtual power plant (VPP) has emerged to aggregate and manage these distributed energy resources (DERs). However, current research on the VPP’s frequency modulation performance and bidding strategy remains insufficient in the joint market of electrical energy and frequency modulation (FM) ancillary services, with inadequate coordination of internally distributed resources. To fully leverage the flexibility of VPPs and incentivize their participation in electricity market operations, this paper investigates game-based bidding strategies and internal distributed resources allocation methods for VPPs in the joint market for electrical energy and frequency ancillary services. Firstly, the regulatory performance indicators of VPPs participating in the joint market and develops the corresponding market-clearing model. Secondly, to address the competition among distributed resources within VPPs, a master-slave game approach is innovatively employed to optimize the VPP’s trading strategies. This method ensures the rational allocation of electricity consumption among distributed energy resources within the VPP and derives the optimized bidding prices and quantities for both the VPP and its internal members. Finally, the case study shows that the proposed trading strategy provides effective bidding strategies for distributed energy resources participating in the joint market for energy and frequency regulation ancillary services. It enhances the regulatory performance of VPPs in the energy-frequency regulation market, ensures the profitability of distributed energy resources, and contributes to the economically stable operation of the market. Full article

With the ongoing integration of renewable energy and energy storage into the power grid, the voltage safety issue has become a significant challenge for the distribution power system. Therefore, this study proposes a coordinated operation for energy storage systems with reactive power compensators. Taking into account the benefits of energy storage equipped with reactive power compensators and the market clearing process, a bi-level optimization model is formulated. In the proposed model, the upper-level model formulates the bidding strategy for energy storage and aims at maximizing the energy storage revenue; the lower-level model carries out a market-clearing process that takes into account various constraints for ensuring the safe operation of the grid. Afterward, by applying several math tricks such as the KKT optimality condition, the strong duality theorem, and the big M method, the bi-level equilibrium programming problem is transformed into an equivalent and tractable single-level mixed-integer linear programming problem. The results show that the coordinated operation of energy storage and reactive power compensators increases the benefit of energy storage by 3.47%. The benefit increment and security improvement brought by the collaborative operation of energy storage and reactive power compensators are verified. Full article

The purpose of this paper is to analyze the profitability of wind energy and demand response (DR) resources participating in the energy and frequency regulation markets. Since wind power producers (WPPs) must reduce their output to provide up-regulation and DR aggregators (DRAs) have to purchase additional power to facilitate down-regulation, this may result in revenue loss. If WPPs coordinate with DRAs, these two costs could be reduced. Thus, it would be profitable for WPPs and DRAs to form a coalition to participate in the regulation market. To better utilize the frequency response characteristics of wind and DR resources, this paper proposes a cooperation scheme to optimize the bidding strategy of the coalition. Furthermore, cooperative game theory methods, including Nucleolus- and Shapley-value-based models, are employed to fairly allocate additional benefits among WPPs and DRAs. The uncertainties associated with wind power and the behavior of DR customers are modeled through stochastic programming. In the optimization process, the decision-maker’s attitude toward risks is considered using conditional value at risk (CVaR). Case studies demonstrate that the proposed bidding strategy can improve the performance of the coalition and lead to higher benefits for both WPPs and DRAs. Specifically, the expected revenue of the coordinated strategies increased by 12.1% compared to that of uncoordinated strategies. Full article

The transportation system has a big impact on daily lifestyle and it is essential to energy transition and decarbonization initiatives. Stabilizing the grid and incorporating sustainable energy sources require technologies like the Internet of Energy (IoE) and Internet of Vehicles (IoV). Electric vehicles (EVs) are essential for cutting emissions and reliance on fossil fuels. According to research on flexible charging methods, allowing EVs to trade electricity can maximize travel distances and efficiently reduce traffic. In order to improve grid efficiency and vehicle coordination, this study suggests an ideal method for energy trading in the Internet of Vehicles (IoV) in which EVs bid for electricity and Road Side Units (RSUs) act as buyers. The Taylor Social Optimization Algorithm (TSOA) is employed for this auction process, focusing on energy and pricing to select the best Charging Station (CS). The TSOA integrates the Taylor series and Social Optimization Algorithm (SOA) to facilitate flow redirection post-trading, evaluating each RSU’s redirection factor to identify overloaded or underloaded CSs. The Flow-DNET model determines redirection policies for overloaded CSs. The TSOA + Flow-DNET approach achieved a pricing improvement of 0.816% and a redirection success rate of 0.918, demonstrating its effectiveness in optimizing electricity trading and flow management within the IoV framework. Full article

This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid. Full article

Background: Increased maritime trade has led to a surge in drayage operations, causing congestion and environmental issues in port areas. Truck Appointment Systems (TASs) are commonly used to manage truck arrival rates, yet transparency and equity in slot allocation remain problematic, fostering distrust between Licensed Motor Carriers (LMCs) and Marine Terminal Operators (MTOs). Methods: This study proposes a polycentric approach to improve truck scheduling and ensure that those impacted by decisions are involved in the decision-making process. A single-round auction mechanism focused on optimizing the truck hauling process through a pricing policy that promotes sincere bidding is introduced. The proposed approach employs an optimization strategy to achieve equitable coordination in truck synchronization through means of adaptable capacity management. Results: Numerical experiments assessing scenarios of noncollaborative behavior against partial collaboration between MTOs and LMCs demonstrate the effectiveness of the proposed approach in enhancing user satisfaction and terminal conditions for a case study focused on a medium-sized terminal. Collaboration between trucking companies is shown to increase utility per monetary unit spent on slot acquisition. Conclusions: The polycentric strategy offers a solution to TAS limitations by ensuring stakeholder participation with respect to flexibility and transparency by ensuring that those impacted by decisions are involved in the decision-making process. Full article

With the rapid development of the Internet of Vehicles (IoV), traffic sign detection plays an indispensable role in advancing autonomous driving and intelligent transportation. However, current road traffic sign detection technologies face challenges in terms of information privacy protection, model accuracy verification, and result sharing. To enhance system sustainability, this paper introduces blockchain technology. The decentralized, tamper-proof, and consensus-based features of blockchain ensure data privacy and security among vehicles while facilitating trustworthy validation of traffic sign detection algorithms and result sharing. Storing model training data on distributed nodes reduces the system computational resources, thereby lowering energy consumption and improving system stability, enhancing the sustainability of the model. This paper introduces an enhanced GGS-YOLO model, optimized based on YOLOv5. The model strengthens the feature extraction capability of the original network by introducing a coordinate attention mechanism and incorporates a BiFPN feature fusion network to enhance detection accuracy. Additionally, the newly designed GGS convolutional module not only improves accuracy but also makes the model more lightweight. The model achieves an enhanced detection accuracy rate of 85.6%, with a reduced parameter count of $0.34\times {10}^7$. In a bid to broaden its application scope, we integrate the model with blockchain technology for traffic sign detection in the IoV. This method demonstrates outstanding performance in traffic sign detection tasks within the IoV, confirming its feasibility and sustainability in practical applications. Full article

With the popularity and promotion of electric vehicles (EVs), virtual power plants (VPPs) provide a new means for the orderly charging management of decentralized EVs. How to set the price of electricity sales for VPP operators to achieve a win–win situation with EV users is a hot topic of current research. Based on this, this paper first proposes a Stackelberg game model in which the VPP participates in the orderly charging management of EVs as a power sales operator, where the operator guides the EV users to charge in an orderly manner by setting a reasonable power sales price and coordinates various distributed resources to jointly participate in the power market. Furthermore, taking into account the impact of wind power output uncertainty on VPP operation, a robust optimization method is used to extend the deterministic Stackelberg game pricing model into a robust optimization model, and a robust adjustment factor is introduced to flexibly adjust the conservativeness of the VPP operator’s bidding scheme in the energy market. The model is then transformed into a robust mixed-integer linear programming (RMILP) problem solved by Karush–Kuhn–Tucker (KKT) conditions and strong dyadic theory. Finally, the effectiveness of the solution method is verified in the calculation example, which gives the optimal pricing strategy for the VPP operator, the optimal charging scheme for EV users, and the remaining internal resources’ contribution plan, providing an important idea for the VPP to centrally manage the charging behavior of EVs and improve its own operating revenue. Full article

The increasing number of residential buildings that are installing distributed energy resources enforces the need for schemes to facilitate a local energy balance. With the continuing evolution of Internet of Things (IoT) technology, Peer-to-Peer (P2P) energy trading is becoming a viable solution to incentivize prosumers and promote efficient energy sharing in a community. This paper develops a model to quantitatively analyze the potential benefits of P2P energy trading for residential buildings that have installed photovoltaic battery systems. The integration of the bidding strategy into a residential energy-management system is feasible to realize cost savings for prosumers. However, the coordination between the bidding strategy and the optimal scheduling of energy has received far too little attention. To better participate in the P2P market, we propose a novel separate bidding energy-management system (SBEMS) that can realize rolling optimal energy scheduling while determining energy bids. The model’s effectiveness is verified via case studies of 75 participants in a community. The results indicate that the prosumers can reduce their costs by up to 24% by employing the proposed SBEMS in the P2P market. In addition, the proposed method is found to offer better performance in terms of economic and technical indices. Full article

In recent years, the ever-increasing research interest in various aspects of the electricity pool-based markets has generated a plethora of complementarity-based approaches to determine participating agents’ optimal offering/bidding strategies and model players’ interactions. In particular, the integration of multiple and diversified market agents, such as conventional generation companies, renewable energy sources, electricity storage facilities and agents with a mixed generation portfolio has instigated significant competition, as each player attempts to establish their market dominance and realize substantial financial benefits. The employment of complementarity modelling approaches can also prove beneficial for the optimal coordination of the electricity and natural gas market coupling. Linear and nonlinear programming as well as complementarity modelling, mainly in the form of mathematical programs with equilibrium constraints (MPECs), equilibrium programs with equilibrium constraints (EPECs) and conjectural variations models (CV) have been widely employed to provide effective market clearing mechanisms, enhance agents’ decision-making process and allow them to exert market power, under perfect and imperfect competition and various market settlements. This work first introduces the theoretical concepts that regulate the majority of contemporary competitive electricity markets. It then presents a comprehensive review of recent advances related to complementarity-based modelling methodologies and their implementation in current competitive electricity pool-based markets applications. Full article