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.

Numerous small-capacity distributed energy resources (DERs) pose technical challenges and increase the management complexity for power system operators. This article proposes an aggregate operation model to efficiently manage a large number of DERs with heterogeneous parameters. The aggregate operation model includes the approximate feasible region and the equivalent operational cost function. First, considering the coupling of power, energy, and regulation service, the operational flexibility of multiple categories of DERs is modeled by a general polytope. Second, by scaling and translating a basic homothetic polytope, a maximum inner aggregation approach is formulated to exploit the potential flexibility of DERs and guarantee solution feasibility. The uncertainty of DERs is modeled by the distributionally robust chance-constrained program (DRCCP), and the equivalent cost function of the DER aggregator is rigidly derived. Last, this study theoretically explains that the aggregate feasible region can be enlarged by introducing the DER clustering process, and a spectral clustering algorithm based on the multi-scale similarity metric method is developed to classify DERs into different aggregators. Numerical simulation results indicate the great potential of the proposed method to enhance system operational flexibility with high computational efficiency.

This article proposes a coordinated operation strategy for a virtual power plant (VPP) with multiple DER aggregators under the wholesale energy and regulation service market. Considering the market clearing process, VPP operation profit, and DER aggregators’ interests, a tri-layer hierarchical framework for VPP is developed to formulate the bidding plans to the superordinate market operator and the dynamic price incentive curves for the subordinate DER owners cooperatively. By using the discrete price quota curves, big-M method, KKT optimal conditions, and strong duality theorem, the tri-layer problem is transformed into an equivalent and tractable mixed integer linear programming problem. Case study with the 141-bus system is conducted to validate the effectiveness of the proposed approach. The advantages of lower cost with operation security guarantee and higher flexibility are demonstrated by comparing with other models.

Controllable distributed resources can offer great potential benefits to the power systems since they possess considerable operation flexibility. However, a high-dimensional mathematical problem is emerged when modeling the massive distributed resources (DRs) with heterogeneous parameters. In light of this, based on the aggregation and disaggregation of massive DRs of small capacity, a model predictive control (MPC) based strategy considering incremental operation cost of various controllable devices is proposed for the real-time secondary frequency regulation in an islanded microgrid. The proposed strategy is implemented in a distributed framework using a neurodynamic-based approach, which only requires the information exchange among neighboring units. Simulation results illustrate that the proposed strategy can efficiently manage massive DRs to maintain the system frequency and achieve a satisfactory economic performance, which indicates its promising application value in the field of microgrid frequency regulation.

In an integrated energy system, the growing number of distributed heat and electric power generation units will bring new technical challenges to the existing centralized economic dispatch strategies. This paper proposes a distributed optimization approach for the economic system operation in a multi-energy system by considering various equality and inequality constraints to accommodate the integration of intermittent renewable generations. The proposed distributed neurodynamic-based approach only requires the information exchange among neighboring units and offers flexibility, adaptivity, scalability, a faster convergence and a lower communication burden compared with some traditional centralized methods. The simulation results of two integrated energy systems validate the effectiveness of the proposed distributed approach. Comparisons with other centralized and distributed optimization methods quantify the advantages of the proposed distributed approach in terms of the convergence speed and computation complexity.