Optimal Scheduling Of Distributed Energy Resources Research Articles

Distributionally Robust Chance-Constrained Transactive Energy Framework for Coupled Electrical and Gas Microgrids

In this article, the transactive energy as a sustainable technology is introduced for the integration of numerous distributed energy resources (DERs) in a reliable manner. DERs can actively participate in the day-ahead (DA), real-time balancing (RTB), and wholesale gas markets for achieving the various goals. This article proposes a distributionally robust chance-constrained (DRCC) model based on the transactive energy approach for the optimal scheduling of microgrids in coupled electrical and gas networks. Optimal scheduling of DERs has been carried out for maximizing the microgrids' profits in the DA electricity and gas markets while minimizing the imbalance costs is an objective for microgrids in the RTB market. Transactive energy technology is used for managing energy exchange between microgrids and the power grid. Moreover, the linearization techniques are employed for avoiding nonlinear equations to obtain reliable results in a short time. Due to the effects of the electrical and gas networks on each other, the interactions between them are considered by investigating both the electrical and gas energy conversions in the real system. Simulation results are extracted considering two cases: Case I without DRCC and Case II with DRCC. Given the results, the cost of interrupted load, DERs, and gas sector in Case II are, respectively, reduced to 35.4%, 25.02%, and 17.1% in comparison with Case I. Moreover, the DRCC method guarantees the achievement of $169027.759 profit for microgrids, which is reduced to 47.85% in comparison with the base case.

Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources in Smart Grids Using an Improved Variable Neighborhood Search

Since the last decade, power systems have been evolving dynamically due to smart grid technologies. In this context, energy management and optimal scheduling of different resources are very important. The main objective of this paper is to study the optimal scheduling of distributed energy resources (OSDER) problem. This problem is a challenging, complex and very large-scale mixed-integer non-linear programming (MINLP) problem. Its complexity escalates with incorporation of uncertain and intermittent renewable sources, electric vehicles, variable loads and markets which makes it hard to be solved using traditional optimization algorithms and solvers. However, it can be handled efficiently and without approximation or modification of the original formulation using modern optimization algorithms such as metaheuristics. In this paper, an improved version of the variable neighborhood search (IVNS) algorithm is proposed to solve the OSDER problem. The proposed algorithm was tested on two large-scale centralized day-ahead energy resource scenarios. In the first scenario, the 12.66 kV, 33-bus test system with a total of 49,920 design variables is used whilst in the second scenario, the 30 kV, 180-bus test system is used with a total of 154,800 design variables. The optimization results using the proposed algorithm were compared with five existing optimization algorithms, i.e., chaotic biogeography-based optimization (CBBO), cross-entropy method and evolutionary PSO (CEEPSO), chaotic differential evolution with PSO (Chaotic-DEEPSO), Levy differential evolution with PSO (Levy-DEEPSO), and the variable neighborhood search (VNS). For the first test system, the IVNS has achieved a score of −5598.89 while for the second test system it has achieved a score of −3180.15. A comparative study of the results has shown that the proposed IVNS algorithm performs better than the remaining algorithms for both cases.