Regarding the potentials of activating commercial consumers in demand response (DR) programs, this paper proposes a new framework for energy scheduling of an active distribution network based on the concept of technical virtual power plant (TVPP), considering operational constraints of distribution network. The TVPP enables presence of commercial buildings and other distributed energy resources (DERs) in day-ahead (DA) electricity market, as a price maker agent. In this regard, a bi-level optimization framework is designed to optimize the bidding strategy of TVPP in DA market with the main goal of maximizing TVPP profit. The upper level problem maximizes the TVPP profit, while in the lower level, market is cleared from independent system operator (ISO) viewpoint. Using Karush-Kuhn-Tucker (KKT) optimality conditions and strong duality theory, proposed bi-level problem is transformed into mixed integer linear programming problem (MILP). Implementing the model on the RBTS test system demonstrates the applicability of the proposed model.

This paper presents a new framework for multistage expansion planning in active power distribution networks, in which the distribution system operator (DSO) considers active network management by clearing the local energy market at the distribution level. The proposed model is formulated as a bi-level optimization problem, where the upper level minimizes the net present value of the total costs imposed to DSO associated with the investment and maintenance of the network assets as well as the network operation, while the lower level on clearing the local energy market captures the participation of distributed energy resource (DER) owners and demand aggregators to maximize the social welfare. The expansion plans consider the investments in DER owners’ assets as well as variety of network assets in which the profitability of DER owners’ investment is guaranteed. The Karush-Kuhn-Tucker optimality conditions and the strong duality theory are employed through which the model is converted to a mixed integer linear programming optimization problem. The implementation of the suggested model on the 24-bus and 86-bus distribution test systems validates its performance and efficacy in making cost-effective planning decisions.

This paper presents a new market-based framework to exploit distributed energy resources' (DERs) flexibility in distribution and transmission systems. This framework targets intra-hourly flexibility requirements of energy systems. In this regard, a local market is operated in distribution network to exploit DERs' capabilities. Local market is cleared in coordination with wholesale market to optimize DERs' flexibility procurement for meeting intra-hourly variability and uncertainty in distribution and transmission systems. In the proposed framework, a technical virtual power plant (TVPP) operates day-ahead (DA) local energy and flexibility markets in which DER aggregators take part. Furthermore, the TVPP participates in DA wholesale energy and flexibility markets which are cleared by the market operator and the transmission system operator, respectively. In this framework, all agents' preferences and their transactions are addressed using a bilevel model with multiple lower levels. The proposed model is reformulated into a mixed integer linear programming problem by utilizing strong duality theory, Karush-Kuhn-Tucker optimality conditions and primal-dual counterpart. The model is implemented on an IEEE standard test system. The results demonstrate effectiveness of the framework in utilizing DERs' capabilities to meet flexibility requirements as well as satisfying agents' preferences.

In this paper, a multi-agent framework is proposed to address the expansion planning problem in a restructured active distribution network. In this framework, the objective and techno-economic constraints of participating agents are addressed in the expansion planning of power network and DER assets as well as the network and DERs optimal operation management. The agents include distributed generator owners and load aggregators which participate along with the distribution network operator (DNO) in the active distribution network planning. The proposed framework is formulated as a bi-level optimization problem with multi-lower levels in which the DNO optimizes the network expansion planning and operation in the upper level where the network reliability is also modeled. In the lower level problems, other participating agents optimize the expansion planning and operation of their assets. The model addresses the optimal operation of resources by clearing the local energy market. The uncertainties in the upper and the lower level problems are effectively addressed using a stochastic adaptive robust optimization approach. The proposed model is implemented on the 54-bus and 138-bus distribution networks and the results demonstrate the effectiveness and scalability of the proposed framework.

Technical virtual power plant (TVPP) aggregates distributed energy resources in order to trade in wholesale electricity market while taking operational constraints of network into account and offering services to distribution system operator. This article presents a new framework to incorporate the reliability of distribution network into designing the optimal bidding strategy of a TVPP in day-ahead energy market. In the proposed framework, the TVPP determines the multisteps biddings as pairs of power and price aiming at maximizing its profit in market while minimizing the outage costs due to contingency occurrences. The model is formulated as a bilevel optimization problem in which the upper level maximizes the TVPP's profit regarding network reliability and the lower level clears the market from ISO's viewpoint. Using Karush–Kuhn–Tucker optimality conditions and strong duality theory, the model is converted to a mixed integer linear programming optimization problem. The proposed framework is implemented on the Roy Billinton test system to illustrate the effectiveness of the model.