Juan Su

With the rapid development of smart cities, coordinating diverse distributed energy resources through storage-centric shared management has become a critical challenge. This paper proposes a bi-level energy management framework to support peer-to-peer energy trading among multiple virtual power plants (VPPs) under multidimensional uncertainties. The interaction is modeled as a Stackelberg–Nash equilibrium framework, in which OK, we will make the necessary revisions as per the requirements.a public energy storage operator and a natural gas company act as leaders to maximize social welfare and design differentiated trading strategies for VPPs. The VPPs act as followers and participate in cooperative energy trading based on a generalized Nash equilibrium scheme, sharing surplus energy and allocating cooperative benefits according to their contributions. To address uncertainty, Conditional Value at Risk (CVaR) is adopted to quantify the expected loss of the upper-level decision makers. The lower-level VPP problem is formulated as a three-stage stochastic robust optimization model considering renewable generation uncertainty. To solve the resulting nonlinear bi-level problem, a two-stage solution approach combining particle swarm optimization and KKT-based reformulation is developed to transform it into a tractable mixed-integer linear programming model. Numerical case studies verify the effectiveness of the proposed framework.

Virtual power plants (VPPs) can aggregate distributed resources across various nodes to participate and collaborate in electricity market trading. Unlike traditional standalone generators or load aggregators, this study leverages the dual role of VPPs as producers and consumers. It introduces a natural risk–hedging mechanism and proposes a price‐acceptance bidding strategy for VPPs in the day‐ahead spot market, which primarily relies on electricity price forecasts. This strategy is compared with scenarios where distributed resources or traditional generators/load aggregators bid independently. The analysis focuses on the success rate of market participation and the actual financial returns. The findings indicate that the proposed strategy based on the natural risk–hedging mechanism substantially enhances the resilience of VPPs in managing market risks and effectively mitigates the negative impacts of price volatility and forecasting errors on their economic benefits.

To enhance the flexibility and economic performance of integrated thermoelectric energy systems, this study develops an optimal operation strategy for supplementary electric heating in heat-exchange stations by explicitly exploiting the virtual thermal energy storage (VTS) potential of district heating networks (DHNs) and buildings. The VTS of the pipe network is modeled based on adjustable supply water temperature, while that of buildings is modeled based on allowable indoor temperature variation, enabling unified scheduling within the heat–electric coupling framework. A coordinated operation model is established for the combined heat and power (CHP) system with supplementary electric heating under time-of-use electricity pricing, aiming to minimize the total operating cost while satisfying thermal comfort and operational constraints. The model is expressed as a mixed-integer linear programming (MILP) problem and solved with the CPLEX optimizer. Case studies demonstrate that the proposed strategy achieves significant cost and carbon-emission reductions: compared with the scenario without VTS, the energy purchase cost decreases by ∼14%, and carbon-emission-related costs are also reduced. Further analysis reveals that the DHN provides a more substantial storage effect (reducing electricity purchases by 1.1%) than buildings (0.1%), highlighting the need to prioritize network heat storage in practical applications. These results confirm the effectiveness, economic advantages, and low-carbon potential of the proposed approach, providing a viable pathway for clean heating systems to reduce dependence on fossil fuels.

The Electromagnetic Transients Program (EMTP) is widely used for simulating electromagnetic transients in power systems. In recent years, applications of electrolyzers in electric power systems have attracted considerable attention because electrolyzers are among the most promising energy-conversion devices for realizing hydrogen production from renewable energy sources while enhancing system flexibility by functioning as controllable loads. With the growing interest in electrolyzers, it would be advantageous to broaden the application of EMTP to include multi-physics transients such as those observed in electrolyzers. This article details the implementation of such an extension for a proton exchange membrane electrolyzer (PEMEL). Analogies between electric, mass transfer, and thermal quantities are adopted to develop an electric circuit model that describes currents, voltages, mass transfer, pressure, heat transfer, and temperature. The PEMEL model considering the interactions of electric, mass transfer, and thermal transients can be readily implemented in EMTP-type programs using existing components from standard libraries. The proposed electric circuit model was validated through comparison with experimental data under both steady-state and transient conditions. The value of the proposed model was illustrated via application to a PEMEL-based integrated electrical and gas system.

Synchronous machines form the principal source of electrical power in power systems. Modeling of the synchronous machines for transient analysis has always been an active topic of research. In this paper, a novel wound-field three-phase synchronous machine model is developed for the accurate and efficient simulation of multi-scale transients. The machine stator equations are expressed with analytic signals in the phase domain, thus providing direct interface between machine and external network models. Frequency shifting is applied to stator quantities to eliminate the ac carrier in the stator windings which enables the use of large time-step size. An artificial damper winding is introduced to eliminate the numerical saliency. To provide accurate and stable solutions with multiple time-step sizes, the artificial winding parameters setting algorithm is established. The proposed machine model is expressed in terms of a Norton equivalent with constant admittance matrix without introduction of prediction of any electrical quantity. The update of the admittance matrix at each time-step is avoided. The analysis of test cases demonstrates the effectiveness of the proposed multiscale synchronous machine model and the artificial winding parameters setting algorithm.

With the large-scale integration of high-penetration distributed energy resources and the transformation of terminal loads into prosumer models, the operation of active distribution networks (ADNs) has become increasingly flexible, often leading to frequent bidirectional power flows. These dynamics pose significant challenges for accurate loss calculation in ADNs and the correction of transaction outcomes between generators and consumers under power market environments. Moreover, the distinct operating structures of the spot and bilateral contract markets further increase the complexity of ADN operation and impose significant challenges on the adaptability of loss allocation (LA) mechanisms. To address these issues, this paper proposes an innovative LA method for ADNs based on Virtual Contribution Theory (VCT), applicable to both spot and bilateral contract market scenarios. By constructing virtual contribution matrices—branch-based for the spot market and transaction-based for the bilateral market—the method accurately traces and quantifies the actual contributions of each market participant under different trading models, thereby enabling reasonable, transparent, and fair loss allocation. Additionally, the method eliminates the need for complex matrix inversion required in existing approaches by leveraging basic operations on the virtual contribution matrix, thereby reducing computational complexity and significantly improving computational efficiency. Case studies on IEEE 33 and IEEE 69 systems demonstrate the effectiveness, applicability, and fairness of the proposed approach.

With the development of the Internet of things and smart grid technologies, modern electricity markets seamlessly connect demand response to the spot market through price-responsive loads, in which the trading strategy of load aggregators plays a crucial role in profit capture. In this study, we propose a deep reinforcement learning-based strategy for purchasing and selling electricity based on real-time electricity prices and real-time demand data in the spot market, which maximizes the revenue of load aggregators. The deep deterministic policy gradient (DDPG) is applied through a bidirectional long- and short-term memory (BiLSTM) network to extract the market state features that are used to make trading decisions. The effectiveness of the method is validated using datasets from the New England electricity market and Australian electricity market by introducing a bidirectional LSTM structure into the actor-critic network structure to learn hidden states in partially observable Markov states through memory inference. Comparative experiments of the method show that the method can provide greater yield results.