The virtual power plant (VPP) provides an efficient way to manage a bunch of distributed energy resources (DERs). In order to participate in the system operation and power market as a whole, the estimation of VPP power flexibility is indispensable. However, the temporal coupling constraints make it intractable to aggregate the power flexibility directly. In this paper, we formulate the flexibility evaluation problem as a chance constrained optimization model incorporated the randomness of renewables. To make it tractable, the evaluated flexibility is represented by the combination of a virtual generator and a virtual battery which is decomposed by a robust optimization algorithm. Then, a novel high-dimensional polytope based bound shrinking method is proposed to estimate the parameters of the virtual generator and virtual battery. The numerical test results show the proposed method obtain much more precise results with lower conservativeness than those of existing methods. The disaggregation feasibility of the evaluation results is also verified.

To unlock the potential of flexible resources, a multi-time-scale economic scheduling strategy for the virtual power plant (VPP) to participate in the wholesale energy and reserve market considering large quantity of deferrable loads (DLs) aggregation and disaggregation is proposed in this paper. For the VPP multi-time-scale scheduling including day-ahead bidding and real-time operation, the following models are proposed, namely, DLs aggregation model based on clustering approach, economic scheduling model considering DLs aggregation, and DLs disaggregation model satisfying consumers' requirements, respectively. The proposed strategy can realize the efficient management of massive DLs to reduce the energy management complexity and increase the overall economics with high computation efficiency, which indicate its promising application in the VPP economic scheduling.

Virtual power plants (VPPs) have become an effective technique to manage a growing number of flexible resources, which have posed great technical challenges to distribution system operators (DSOs). This paper proposes a bi-level programming approach for the collaborative management of an active distribution network (ADN) with multiple VPPs by designing comprehensive prices for active and reactive power. The upper layer aims to minimize the overall operation cost of the ADN considering the system security and economic operation and the interactions among the power market, ADN and VPPs. The lower layer aims to maximize the benefits of each VPP agent considering various flexible resources. Then, the bi-level model is transformed into a tractable single-level problem by using a linearization method, the Karush—Kuhn—Tucker (KKT) optimality conditions, the Fortuny-Amat transformation and the strong duality theorem. Case analyses indicate that the proposed strategy can effectively enhance the system security and improve the system economic performance by introducing reactive power pricing. The implementation effect and superiority of the proposed strategy are profoundly analyzed under different scenarios and conditions, which indicates its promising application value in the industrial field.

The operational flexibility of electric power systems (EPS) is restricted by combined heat and power (CHP) units that act to maintain sufficient heating supply. By exploiting the pipeline heat storage property in central heating systems (CHS), combined heat and power dispatch (CHPD) can significantly increase the operational flexibility and reduce wind power curtailment. In this paper, pipeline heat storage is modeled in the CHPD model under the constant mass flow heating dispatch mode, and the CHPD model can be formulized as a quadratic programming problem. Since the EPS and CHS are independently operated by the EPS and CHS operators, a decentralized solution to the CHPD model is proposed. During each iteration of the decentralized procedure, an optimal cut or a feasible cut is generated by the CHS operator and sent to the EPS operator. A robust model considering the wind power uncertainty is also studied with the proposed decentralized solution. This decentralized solution has a high efficiency and a light communication burden. Numerical tests on practical systems demonstrate the feasibility of the proposed decentralized method and the economic benefits brought by reducing wind curtailment.

State estimation (SE) is indispensable for the situational awareness of power systems. Conventional SE is fed by measurements collected from the supervisory control and data acquisition (SCADA) system. In recent years, available data sources have been greatly enriched with the deployment of phasor measurement units (PMUs), advanced metering infrastructure (AMI), intelligent electronic devices (IEDs), etc. The integration of multiple data sources provides unprecedented opportunities for enhancing the performance of SE, but also presents major challenges to resolve, including optimal multi-type-sensor co-placement, multiple reporting rates and asynchronization, diverse types of measured quantities, correlations between measurements, integration of online and historical data sources, and system and measurement uncertainties. This paper outlines the state of the art and research opportunities in this area by providing a comprehensive literature review and extensive discussions. It starts by presenting the motivations and challenges, followed by a summary of existing data sources for SE in power systems. Subsequently, for both transmission system (static and dynamic) and distribution system SE, existing methods are systematically reviewed and categorized based on the addressed challenges. Interesting attempts of using novel measurements in SE are also studied. Finally, the paper concludes by providing a detailed discussion on the remaining research gaps and future research directions to be explored.

This paper proposes a new distance-based distributionally robust unit commitment (DB-DRUC) model via Kullback-Leibler (KL) divergence, considering volatile wind power generation. The objective function of the DB-DRUC model is to minimize the expected cost under the worst case wind distributions restricted in an ambiguity set. The ambiguity set is a family of distributions within a fixed distance from a nominal distribution. The distance between two distributions is measured by KL divergence. The DB-DRUC model is a “min-max-min” programming model; thus, it is intractable to solve. Applying reformulation methods and stochastic programming technologies, we reformulate this “min-max-min” DB-DRUC model into a one-level model, referred to as the reformulated DB-DRUC (RDB-DRUC) model. Using the generalized Benders decomposition, we then propose a two-level decomposition method and an iterative algorithm to address the RDB-DRUC model. The iterative algorithm for the RDB-DRUC model guarantees global convergence within finite iterations. Case studies are carried out to demonstrate the effectiveness, global optimality, and finite convergence of a proposed solution strategy.