Search Results (5)

To address the limited flexibility of pumped storage power stations (PSPSs) under hierarchical clearing of energy and ancillary service markets, this study proposes a joint clearing mechanism for multi-level electricity markets. A bi-level optimization model based on the Stackelberg game is developed to characterize the strategic interaction between PSPSs and the market operator. Simulation results on the IEEE 30-bus system demonstrate that the proposed mechanism captures the dynamics of nodal supply and demand, as well as time-varying network congestion. It guides PSPSs to operate more flexibly and economically. Additionally, the mechanism increases PSPS profitability, reduces system costs, and improves frequency regulation performance. This game-theoretic framework offers quantitative decision support for PSPS participation in multi-level spot markets and provides insights for optimal storage deployment and market mechanism improvement. Full article

The global energy transition is accelerating, bringing new challenges to power systems. A high penetration of renewable energy increases grid volatility. Virtual power plants (VPPs) address this by dynamically responding to market signals. They integrate renewables, energy storage, and flexible loads. Additionally, they participate in multi-tier markets, including energy, ancillary services, and capacity trading. This study proposes a load factor-based VPP pre-dispatch model for optimal resource allocation. It incorporates the coupling effects of electricity–carbon markets. A Nash negotiation strategy is developed for multi-VPP cooperation. The model uses an accelerated adaptive alternating-direction multiplier method (AA-ADMM) for efficient demand response. The approach balances computational efficiency with privacy protection. Revenue is allocated fairly based on individual contributions. The study uses data from a VPP dispatch center in Shanxi Province. Shanxi has abundant wind and solar resources, necessitating advanced scheduling methods. Cooperative operation boosts profits for three VPPs by CNY 1101, 260, and 823, respectively. The alliance’s total profit rises by CNY 2184. Carbon emissions drop by 31.3% to 8.113 tons, with a CNY 926 gain over independent operation. Post-cooperation, VPP1 and VPP2 see slight emission increases, while VPP3 achieves major reductions. This leads to significant low-carbon benefits. This method proves effective in cutting costs and emissions. It also balances economic and environmental gains while ensuring fair profit distribution. Full article

This study proposes a multi-objective optimization framework for peer-to-peer (P2P) energy trading in South Korea’s tiered electricity pricing system. The framework employs the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) to optimize three conflicting objectives: minimizing consumer costs, maximizing prosumer benefits, and enhancing energy utilization. Using real microgrid data from a South Korean community, the framework’s performance is validated through simulations. The results highlight that MOEA/D achieved an optimal cost of KRW 32,205.0, a benefit of KRW 32,205.0, and an energy utilization rate of 57.46%, outperforming the widely used NSGA-II algorithm. Pareto front analysis demonstrates MOEA/D’s ability to generate diverse and balanced solutions, making it well suited for regulated energy markets. These findings underline the framework’s potential to improve energy efficiency, lower costs, and foster sustainable energy trading practices. This research offers valuable insights for advancing decentralized energy systems in South Korea and similar environments. Full article

To fully leverage the potential flexibility resources of a source-network-load-storage (SNLS) system and achieve the green transformation of multi-source systems, this paper proposes an economic and low-carbon operation strategy for an SNLS system, considering the joint operation of ladder-type green certificate trading (GCT)–carbon emission trading (CET), and integrated demand response (IDR). Firstly, focusing on the load side of electricity–heat–cooling–gas multi-source coupling, this paper comprehensively considers three types of flexible loads: transferable, replaceable, and reducible. An IDR model is established to tap into the load-side scheduling potential. Secondly, improvements are made to the market mechanisms: as a result of the division into tiered intervals and introduction of reward–penalty coefficients, the traditional GCT mechanism was improved to a more constraining and flexible ladder-type GCT mechanism. Moreover, the carbon offset mechanism behind green certificates serves as a bridge, leading to a GCT-CET joint operation mechanism. Finally, an economic low-carbon operation model is formulated with the objective of minimizing the comprehensive cost consisting of GCT cost, CET cost, energy procurement cost, IDR cost, and system operation cost. Simulation results indicate that by effectively integrating market mechanisms and IDR, the system can enhance its capacity for renewable energy penetration, reduce carbon emissions, and achieve green and sustainable development. Full article

In the existing research on the dispatch and control strategies of park micro-energy grids, the dispatch and control characteristics of controllable energy units, such as response delay, startup and shutdown characteristics, response speed, and sustainable response time, have not been taken into account. Without considering the dispatch and control characteristics of the controllable energy units, substantial deviation will occur in the execution of optimized dispatch and control strategies, resulting in economic losses in the electricity market and adverse effects on the safe operation of power systems. This paper proposes a unified model to describe the dispatch and control characteristics of various types of controlled energy units, based on which we develop a three-tier optimization dispatch and control strategy for the micro-energy grid, involving day-ahead, intra-day, and real-time stages. The day-ahead and intra-day optimization dispatch strategy is implemented to obtain the optimal reference values in the real-time stage for each controllable energy unit. In the real-time stage, a minimum variance control strategy based on d-step prediction is proposed. By considering the multi-dimensional control characteristics of controllable energy units, the real-time predictive control strategy aims to ensure that the controllable energy units can precisely follow the optimized dispatch plan. The simulation results show that when compared with the dispatching method optimized by the improved quantum particle swarm algorithm, the adoption of the optimal dispatch and control strategy proposed in this paper resulted in a 45.79% improvement in execution accuracy and a 2.38% reduction in the energy cost. Full article