In this paper, we propose a distributionally robust chance constrained (DRCC) optimization problem for the operation of an active distribution network (ADN). The ADN’s operator uses the proposed problem to centrally optimize the dispatch plan of his resources, namely photovoltaic (PV) and battery energy storage (BES) systems, and to participate in wholesale real/reactive power and flexibility markets. We model the uncertainties in the problem by knowing a set of probability distributions, i.e., an ambiguity set. The uncertainties include production capability of PV systems, end-users’ consumption, requested flexibility by the external network’s operator, and voltage magnitude at the point of common coupling (PCC). The resulting formulation is a DRCC optimization problem for which a solution methodology based on freely available solvers is presented. We evaluate the performance of proposed solution in the numerical results section by comparing it with two benchmark models based on stochastic and chance constrained (CC) optimization.

Multicarrier energy systems have provided the opportunity for users who are experiencing only must-run loads to participate in demand response programs by using self-generation units. In this paper, a bilevel game between so-called energy retailers and consumers with firm loads (as subsystems) is formulated in a multicarrier energy system. In the first level of this game, energy retailers offer selling prices for different energy carriers to maximize their profit. Consequently, consumers react to the offered prices by managing their purchasing power from the retail market to minimize their energy bill in the second level. The proposed approach leads to a mixed integer nonlinear programming problem which is sophisticated to solve. To simplify this complex problem, a solution method based on discretization technique is introduced to approach to an epsilon Nash point. The better performance of the proposed energy system model is shown in comparison with the base case and its effectiveness is validated experimentally over a practical benchmark system. The simulation results show a noticeable reduction in the energy production cost and users’ energy bill. Moreover, energy retailers gain more profit in the proposed approach.

One of the main shortcomings, caused by high penetration of wind power, is intermittency of generation. For integrating high penetration of wind power, the frequency regulation and the transactive control systems are modified to be sufficiently resilient against fluctuations of wind power and malicious cyber threats. Here, a hierarchical state-space model is presented for the frequency regulation and the transactive control systems in a smart grid environment. To achieve a resilient control, a framework based on cloud computing is proposed for the communication network. Benefits and challenges of the cloud-based framework are also described in this paper. To optimize the operation of the proposed framework, control parameters are tuned robustly by using the edge theorem. Finally, the performance of proposed control system is evaluated in the case study section.