Zibo Wang

With the rapid development and widespread deployment of advanced metering infrastructures, massive amounts of fine-grained data about aggregated residential load have been collected by power network operators, further helping improve forecasting accuracy. Accurate aggregated residential load forecasting plays an increasingly important role in various interactions between power networks and electricity customers as well as in maintaining the continuous balance between electricity supply and demand. However, most existing short-term aggregated residential load forecasting methods only take into consideration the temporal correlation of historical load profiles, ignoring the potential spatial correlation between electricity consumption behaviors of adjacent residential customers. Thus, to fill this gap, this paper proposes a short-term aggregated residential load forecasting method based on graph neural networks and K-means clustering. Specifically, K-means clustering is firstly used to divide residential customers into different groups according to the similarity of their electricity consumption behaviors. Then, the spatial–temporal graph series for aggregated residential load forecasting is constructed, based on the number of groups of residential customers, the aggregated historical load profile of each group of residential customers, and the correlation between the aggregated historical load profiles of different groups of residential customers. Finally, adaptive spatial–temporal synchronous graph convolutional networks are applied to perform short-term aggregated residential load forecasting. The proposed method is evaluated on a real-life Irish residential load dataset, and the experimental results demonstrate that it can improve forecasting accuracy significantly in comparison with a number of traditional benchmark methods.

Constrained by current market mechanisms, small-scale virtual power plants (SS-VPPs) on the distribution network side struggle to exert their market characteristics. To address this, this paper proposes a trading framework and operational strategy for distribution-side SS-VPPs to participate in the day-ahead energy market. First, an operation and trading framework for distribution networks involving SS-VPPs is proposed. This framework comprehensively considers the clearing process of the electricity energy market, the operation mechanism of the distribution network, and the cost structures of various stakeholders, while clarifying the day-ahead market clearing mechanism at the distribution network level. Next, accounting for energy balance constraints and distribution network congestion constraints, this paper establishes a collaborative optimization model between SS-VPPs and active distribution networks. After obtaining the energy optimization results for all stakeholders, distribution locational marginal pricing (DLMP) is determined based on the dual problem solution to achieve multi-stakeholder market clearing. Finally, simulations using a modified IEEE 33-node test system demonstrate the rationality and feasibility of the proposed strategy. The framework fully exploits the market characteristics and dispatch potential of SS-VPPs, significantly reduces overall system operating costs, and ensures the economic benefits of all participants.

Household loads are becoming dominant in virtual power plants (VPP). However, their dispatch potential has not yet been explored due to the lack of detailed user power management. To solve this issue, a novel two-layer user energy management strategy based on HG-multi-agent reinforcement learning has been proposed in this paper. Firstly, a novel two-layer optimization framework is established, where the upper layer is applied to coordinate the scheduling and benefit allocation among various stakeholders and the lower layer is applied to execute intelligent decision-making for users. Secondly, the mathematical model for the framework is established, where a detailed household power management model is proposed in the lower layer, and the generated predicted power demands are used to replace the conventional aggregate model in the upper layer. As a result, the energy consumption behaviors of household users can be precisely described in the scheduling scheme. Furthermore, an HG-multi-agent reinforcement-based method is applied to accelerate the game-solving process. Case study results indicate that the proposed method leads to a reduction in user costs and an increase in VPP profit.