As the cost of the battery energy storage system (BESS) is lower, the penetration rate of battery storage is rising in the behind-the-meter (BTM) market. BESS with time-of-use rates (TOU) for charge and discharge scheduling can be used to reduce electricity costs. This research uses 6,600KW contract capacity for industrial customers as the study case. Through the BESS techno-economic simulation in Hybrid Optimization Models for Energy Resources (HOMER) Grid software, the optimal capacity planning can be obtained from the electricity demand. The results show that the contract capacity can be adjusted to 6,100kW with a project benefit of $68,557, levelized cost of energy (LCOE) of 0.09338 $/kWh, net present cost (NPC) of $−$24,768,508, internal rate of return (IRR) of 3.72%, return of investment (ROI) of 2.16%, and discounted payback of 8.37 years in the 500kW/1065kWh required BESS capacity case. The sensitivity of the electricity price, key components cost, and real interest rate are evaluated and the method will be applied as a reference for planning the BTM BESS of other utility customers.

Different inverters types have different technical specifications and costs which contribute to the changes of inverter type decisions. It is therefore important to know which inverter is more advantageous for certain criteria. In this paper, the central, string, multi-string, and AC module types of inverters were compared through optimally designing a DG-PV and DG-PV-battery system of an off-grid island using each type of inverter. The problem was solved as a multi-objective optimization considering cost and uncertainty. The results were compared in terms of cost, reliability, renewable energy penetration, renewable energy curtailment, and changes in battery cost. The optimization results showed that the string inverter is best in terms of cost and highly affected by the decrease of the battery cost, the AC module in terms of reliability and curtailment, and the multi-string for renewable energy penetration. Meanwhile, the central inverter can be the choice when a balance between cost and reliability is considered.

This paper investigates the feasible reserve of diesel generators in day-ahead unit commitment (DAUC) in order to handle the uncertainties of renewable energy sources. Unlike other studies that deal with the ramping of generators, this paper extends the ramp rate consideration further, using dynamic limits for the scheduling of available reserves (feasible reserve) to deal with hidden infeasible reserve issues found in the literature. The unit commitment (UC) problem is solved as a two-stage day-ahead robust scenario-based unit commitment using a metaheuristic new variant of particle swarm optimization (PSO) called partitioned step PSO (PSPSO) that can deal with the dynamic system. The PSPSO was pre-optimized and was able to find the solution for the base-case UC problem in a short time. The evaluation of the optimized UC schedules for different degrees of reserve consideration was analyzed. The results reveal that there is a significant advantage in using the feasible reserve formulation, especially for the deterministic approach, over the conventional computation in dealing with uncertainties in on-the-day operations even with the increase in the reserve schedule.

The partitioned step particle swarm optimization (PSPSO) introduces a two-fold searching mechanism that increases the search capability of Particle Swarm Optimization. The first layer involves the γ and λ, values which are introduced to describe the current condition of characteristics of the searched solution that diversifies the particles when it is converging too much on some optima. The second layer involves the partitioning of particles that tries to prevent premature convergence. With the two search mechanisms, the PSPSO presents a simpler way of making the particles communicate with each other without too much compromise of the computational time. The proposed algorithm was compared with different variants of particle swarm optimization (PSO) using benchmark functions as well as the IEEE 10-unit unit commitment problem. Results proved the effectiveness of PSPSO with different functions and proved its competitive advantage in comparison with published PSO variants.