Metaheuristics and Transmission Expansion Planning: A Comparative Case Study

Abstract

Transmission expansion planning (TEP), the determination of new transmission lines to be added to an existing power network, is a key element in power system planning. Using classical optimization to define the most suitable reinforcements is the most desirable alternative. However, the extent of the under-study problems is growing, because of the uncertainties introduced by renewable generation or electric vehicles (EVs) and the larger sizes under consideration given the trends for higher renewable shares and stronger market integration. This means that classical optimization, even using efficient techniques, such as stochastic decomposition, can have issues when solving large-sized problems. This is compounded by the fact that, in many cases, it is necessary to solve a large number of instances of a problem in order to incorporate further considerations. Thus, it can be interesting to resort to metaheuristics, which can offer quick solutions at the expense of an optimality guarantee. Metaheuristics can even be combined with classical optimization to try to extract the best of both worlds. There is a vast literature that tests individual metaheuristics on specific case studies, but wide comparisons are missing. In this paper, a genetic algorithm (GA), orthogonal crossover based differential evolution (OXDE), grey wolf optimizer (GWO), moth–flame optimization (MFO), exchange market algorithm (EMA), sine cosine algorithm (SCA) optimization and imperialistic competitive algorithm (ICA) are tested and compared. The algorithms are applied to the standard test systems of IEEE 24, and 118 buses. Results indicate that, although all metaheuristics are effective, they have diverging profiles in terms of computational time and finding optimal plans for TEP.

Transmission Expansion Planning Considering Storage, Flexible AC Transmission System, Losses, and Contingencies to Integrate Wind Power

“…In addition, several optimization techniques are proposed in the literature to solve the TEP problem, such as exact methods and approximate methods (heuristic and metaheuristic techniques) [21]. Within the exact methods are mixed-integer linear programming, nonlinear programming, branch and bound, branch and cut, dynamic programming, and decomposition methods, and within the approximate methods are genetic algorithms [22], ant colony optimization [23], and others [24].…”

Đề xuất phương pháp lai cho bài toán tối ưu vị trí và dung lượng hệ thống lưu trữ năng lượng trong quy hoạch mở rộng truyền tải điện

“…Đối với thuật toán GA, phương pháp giải bài toán tối ưu tham khảo theo [29] và dữ liệu áp dụng trong trường hợp lưới điện IEEE 24 bus tham khảo [29,30]. Tóm tắt một số thông số đầu vào cho ví dụ này gồm: khởi tạo với 20 biến số ngẫu nhiên ban đầu; tối đa 50 biến số trong quần thể; tỷ lệ lai tạo là 0,8; phương pháp lựa chọn lai tạo là "Roulette Wheel"; tỷ lệ đột biến 0,2.…”

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

“…The model assumes that the EV relevant parameters obey the above distribution, e.g., leaving home in the morning to stop charging and returning home at night to start charging. Reference [65] takes load samples by MCS and estimates the total average weight by a discrete probability formula, while [66] first selects whether the EV is V2G through roulette, then generates random numbers, and finally uses MCS to produce massive random scenarios. Reference [67] discretizes EV charging duration and charging-start time, and obtains joint uncertainty by Cartesian product.…”