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Published in:
06-07-2024 | Original Paper
Reliability-constrained transmission expansion planning based on simultaneous forecasting method of loads and renewable generations
Authors: Mohammad Hossein Oboudi, Hamidreza Hamidpour, Mahmoud Zadehbagheri, Sheila Safaee, Sasan Pirouzi
Published in: Electrical Engineering | Issue 1/2025
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Abstract
The article introduces a novel approach for reliability-constrained transmission expansion planning (RCTEP) based on simultaneous forecasting of loads and renewable energy sources (RES). It addresses the limitations of existing methods by incorporating a time series-based logistic method (TSBLM) for net power demand forecasting and using a hybrid grey wolf optimization (GWO) and teaching-learning-based optimization (TLBO) algorithm for solving the RCTEP problem. The proposed method considers the uncertainties in RES power generation, load factor, and network equipment availability, ensuring reliable and secure solutions. The article also presents illustrative examples and case studies on the IEEE 30-bus and 118-bus transmission networks, demonstrating the effectiveness of the proposed framework in improving voltage profile, reducing network losses, and enhancing reliability indices. The convergence results of different solvers highlight the superior performance of the GWO+TLBO algorithm in achieving optimal solutions. The article concludes by discussing future research directions, including the consideration of reactive power compensators and the net present value of planning costs.
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Abstract
Due to increased energy consumption in upcoming years, the power system needs to be expanded to meet suitable technical conditions. The primary requirement is to gain accurate information about consumption growth in the planning horizon, which can be obtained via forecast studies. Since renewable sources can grow beside the demand, the accurate prediction should consider simultaneous changes in supply and demand in the future. In this paper, a reliability-constrained transmission expansion planning (RCTEP) is proposed. It simultaneously is based on the load forecasting and renewable sources production, named the net power demand forecasting technique (NPDFT). NPDFT consists of a time series-based logistic method, which forecasts loads at planning years. RES generation forecasting forecasts the following year’s generation by an estimated coefficient. RCTEP minimizes the summation of the planning, operation, and reliability cost so that it is limited to the AC optimal power flow equations, planning constraints, and reliability limitations for N – 1 contingency. Then, the stochastic programming based on the Monte Carlo Simulation and the simultaneous backward approach models the uncertainties of the load, RES power, and availability of network equipment. This problem is solved by the hybrid algorithm of grey wolf optimization and training and learning optimization algorithm to achieve the securable optimal solution with a low standard deviation. Generally, this paper contributes to predicting the net power demand, simultaneous modeling of operation, reliability, and economic indices, besides using hybrid algorithms to solve the defined problem. Finally, this strategy is implemented on the 3-bus, 30-bus, and 118-bus transmission networks in MATLAB software. The numerical results confirm the capabilities of the proposed method in improving network operation and reliability indices. Higher reliability can be found for the network by defining a desirable penalty price. Also, operation indices, such as voltage profile and power loss, increase more than 10% under these conditions.
