Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

Abstract

This paper introduces a robust optimal week-ahead generation scheduling approach that takes into account plug in hybrid electric vehicles (PHEVs) considering uncertainty in loads, renewable energy resources, and PHEV charging behavior. Due to the complexity of the scheduling process there is crucial need for a reliable optimal algorithm. The proposed approach can be applied in energy management platforms of decarbonized eco-friendly power systems. Generation scheduling is modeled as a multi-objective optimization problem: (a) minimize generation production cost and (b) minimize emission costs. The focal concern is to (a) handle the scheduling of renewable energy resources against their volatilities, (b) integrating PHEVs with uncertainties related to their state of charge, and (c) stochastic load behavior over a whole week. Two heuristic-based algorithms are used to solve the optimization problem, namely Water Cycle Algorithm and Gravitational Search Algorithm The proposed scheduling approach is implemented in MATLAB ® Platform, and is tested using two different microgrids sizes, 3 generator, and 10 generator unit systems integrating the effect of week days profile, renewable energy intermittency and different PHEV state of charges using the IEEE Reliability Test System (RTS) data. The results show promising performance of GSA over the WCA in the energy management studies integrating three different types of sources; thermal units, Renewable Energy Resources (RERs), and the PHEVs.

Integration of Renewable Energy and Electric Vehicles in Power Systems: A Review

“…[150] Optimal designing and execution of power networks integrating RESs and hydrogen fuel cell vehicles. Reference Contribution [151] A robust optimal week-ahead generation scheduling methodology considering renewable energy resources, plug-in hybrid EV behavior, and load unpredictability, as well as two heuristic-based algorithms, the gravitational search algorithm and the water cycle algorithm.…”

“…Plug-in hybrid EVs [39,44,46,53,58,82,151,159,173] Full EVs [21][22][23][24][25][27][28][29][30][31][32][33][34][35][36][37][38]40,42,43,45,[47][48][49][50][51][52][54][55][56][57][59][60][61][62][63][64][65][66][67][68][69][70]…”

“…Particle swarm optimization [66,88,112,121,128,160,162,164,165,191] Genetic algorithm [36,76,129,183] Virus colony search algorithm [84] Harris hawks algorithm [38] Grey wolf optimization algorithm [96] Cuckoo search algorithm [98] Artificial bee colony algorithm [102,128] Extreme learning machine algorithm [107] Alternating direction method of multipliers [111] Model predictive control learning [116] Parameter adaptive differential evolution algorithm [117] Self-adaptive imperialist competitive algorithm [148] Gravitational search algorithm [151] Table 9. Cont.…”

“…Water cycle algorithm [151] Artificial ecosystem optimization algorithm [157] Soft actor-critic deep reinforcment learning algorithm [169] Autoregressive moving average algorithm [178] Spike neural network learning algorithm [188] Golden eagle algorithm [188] Table 10. Optimization tools used in the literature.…”

Charge Scheduling Optimization of Plug-In Electric Vehicle in a PV Powered Grid-Connected Charging Station Based on Day-Ahead Solar Energy Forecasting in Australia

“…In [30], the paper illustrates a two-stage energy scheduling in office buildings with PV systems and workplace EV charging. Based on a two-stage model, the work reported in [31] examines the influence of high renewable sources and electric vehicle penetration on generation scheduling and overall cost. A reliable, optimal week-ahead generation scheduling technique for Plug-in Hybrid Electric Vehicles (PHEVs) is provided in [32], which takes into account unpredictability in loads, renewable energy sources, and PHEV charging behavior.…”