Active power management of islanded interconnected distributed generation

Abstract: The present paper proposes a management of active power in distributed generation considering an islanded mode. Power system is a complex system from the point of view of its constitution, operation and management. Because of energy sources scarcity and energy increasing demand in most of the electrical power systems worldwide, renewable energy exploitation continue to attract researches and exploitation of this weather depending resources. When considering the island mode or without connection to the main grid, of the distributed generation its operation and control became more difficult or uncertain based their dependencies on the weather. Using optimal theory, this paper solve the management of interconnected microgrids operating in islanded mode. Matlab software is used to solve all optimisation problems

A review of optimal operation of microgrids

The term microgrid refers to small-scale power grid that can operate autonomously or in concurrence with the area’s main electrical grid. The intermittent characteristic of DGs which defies the power quality and voltage manifests the requirement for new planning and operation approaches for microgrids. Consequently, conventional optimization methods in new power systems have been critically biased all through the previous decade. One of the main technological and inexpensive tools in this regard is the optimal generation scheduling of microgrid. As a primary optimization tool in the planning and operation fields, optimal operation has an undeniable part in the power system. This paper reviews and evaluates the optimal operation approaches mostly related to microgrids. In this work, the foremost optimal generation scheduling approaches are compared in terms of their objective functions, techniques and constraints. To conclude, a few fundamental challenges occurring from the latest optimal generation scheduling techniques in microgrids are addressed

Interactive energy management for networked microgrids with risk aversion

Department of Finance and Education of Guangdong Province 2016[202]: Key Discipline Construction Programme, China; Guangdong Foshan Power Construction Corporation Group Co. Ltd., Foshan, China

불확실한 독립운전 상황을 고려한 마이크로그리드 운영계획 최적화

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2019. 2. 이경식.본 논문은 마이크로그리드 운영자 관점에서 불확실한 독립운전 상황에 대비한 운영계획 최적화 문제를 다룬다. 마이크로그리드는 전력 수요의 상당 부분을 상위 송전계통에 의존하기 때문에, 송전계통과의 연결이 중단되는 독립운전에 적절히 대처하지 못하였을 경우 경제적으로 상당한 손실이 발생할 수 있다. 본 연구에서는 주어진 계획기간 중 발생 가능한 독립운전 사건의 집합과 각 사건의 발생 가능성을 확률로 정의하여 이를 다단계 추계적 최적화 모형으로 제시한다. 또한, 이러한 규모가 큰 혼합 정수 최적화 모형을 풀기 위한 해법으로 다양한 2단계 벤더스 분해 기법과 분해 기반 휴리스틱을 제안한다. 실험을 통해 본 논문에서 제시한 모형이 예비력을 통해 위험 상황을 대비하는 방법과, 불확실성에 대비를 하지 않는 방법에 비해 독립운전 발생 시의 비용을 절감할 수 있음을 확인하였다. 또한, 제시한 분해 기법과 휴리스틱이 일반 해법에 비해 규모가 큰 문제에 대해 효과적임을 확인하였다.In this thesis, we consider how to optimize microgrid operators operation plans in the context of uncertain islanding events. Due to the dependence of microgrid electricity demands on the main transmission system, significant financial losses can occur if the microgrid does not cope well with islanded operation, during which it is disconnected from the transmission system. A multistage stochastic optimization model is presented in this thesis. We define the set of possible islanding events during a given planning horizon, and the probability of each event. Various two-stage Benders decomposition methods and decomposition-based heuristics are proposed to solve this large-scale mixed integer optimization model. Experiments are conducted to show that the model and strategy presented in this paper can reduce the cost of islanded operation, compared with the methods based on preparing for uncertainties by defining reserve requirements or those with no preparation for uncertainty. We also found that the proposed decomposition and heuristic techniques were effective for large-scale instances than the extensive formulation approach.Chapter 1 Introduction 1 1.1 Background 1 1.1.1 Operation Planning in Power Systems 1 1.1.2 A Microgrid 3 1.2 Problem Description 5 1.3 Literature Review 6 1.3.1 Uncertainty in the UC Problem 6 1.3.2 Uncertainties in Microgrids 8 1.4 Motivations and Contributions 11 1.5 Organization of the Thesis 12 Chapter 2 Mathematical Formulations 13 2.1 Basic Formulation with No Islanding 13 2.2 Extensive Formulation considering Islanding Uncertainty 18 Chapter 3 Decomposition Approaches & Heuristics 25 3.1 Benders Decomposition Method 28 3.2 Various Decomposition Options 33 3.3 Decomposition-based Heuristics 38 Chapter 4 Computational Analysis 39 4.1 Test Instances 39 4.2 Cost & Sensitivity Analysis 42 4.2.1 Cost Analysis 42 4.2.2 Sensitivity Analysis 45 4.3 Performance of Solution Approaches 49 Chapter 5 Conclusion 55Maste

A Stochastic-Robust Approach for Resilient Microgrid Investment Planning Under Static and Transient Islanding Security Constraints

When planning the investment in Microgrids (MGs), usually static security constraints are included to ensure their resilience and ability to operate in islanded mode. However, unscheduled islanding events may trigger cascading disconnections of Distributed Energy Resources (DERs) inside the MG due to the transient response, leading to a partial or full loss of load. In this paper, a min-max-min, hybrid, stochastic-robust investment planning model is proposed to obtain a resilient MG considering both High-Impact-Low-Frequency (HILF) and Low-Impact-High-Frequency (LIHF) uncertainties. The HILF uncertainty pertains to the unscheduled islanding of the MG after a disastrous event, and the LIHF uncertainty relates to correlated loads and DER generation, characterized by a set of scenarios. The MG resilience under both types of uncertainty is ensured by incorporating static and transient islanding constraints into the proposed investment model. The inclusion of transient response constraints leads to a min-max-min problem with a non-linear dynamic frequency response model that cannot be solved directly by available optimization tools. Thus, in this paper, a three-stage solution approach is proposed to find the optimal investment plan. The performance of the proposed algorithm is tested on the CIGRE 18-node distribution network