This paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) for grid-connected households to minimize the net present cost of electricity. Real-time rule-based home energy management systems using actual annual data of solar insolation, ambient temperature, household electricity consumption and electricity rates are used in the optimization process. The above technique is applied to two system configurations — household with a solar PV, and household with a solar PV and a BES. The uncertainty analysis is implemented using 10 years real data to confirm the optimal results. An accurate cash flow analysis is also presented to illustrate the customer payment in each year during the project lifetime. Sensitivity analysis are conducted by varying the cost and capacity of system components, grid constraint, average daily electricity demand, and retail price of electricity. A typical grid-connected household in South Australia is considered as the case study. A practical guideline is presented for the residential consumers in South Australia to select the optimal PV/BES based on their daily average electricity demand and the available rooftop space for PV installation. Finally, the proposed optimization method is applied to households of other Australian States and a comparison of results is presented.

Integration of renewable and energy storage components in standalone/grid‐connected energy systems, which results in hybrid energy systems, is increasing nowadays. Optimisation of hybrid energy systems is an essential matter for economic, clean, convenient and reliable energy supply. Since the optimal design should satisfy multiple objectives, application of multi‐objective optimisation is preferred rather than a single‐objective solution. By multi‐objective optimisation, a trade‐off among different objectives can be obtained. This study presents a timely survey on the state‐of‐the‐art in multi‐objective optimal design of hybrid standalone/grid‐connected energy systems. The existing literature is categorised by various indices: (i) standalone or grid‐connected mode; (ii) number and type of objective functions; (iii) completely renewable‐based or diesel‐renewable‐based hybrid systems; and (iv) multi‐energy systems. The applied objective functions, design constraints and decision variables in the optimisation of hybrid energy systems are addressed. It is found that the recent objective functions like customer satisfaction and grid‐dependency need further investigation in the multi‐objective design procedure.

This paper develops a novel demand side management (DSM) approach to incorporate in optimal sizing of solar photovoltaic (PV), wind turbine (WT), and battery storage (BS) for a standalone household. The DSM strategy is based on the state-of-charge level of battery and day-ahead forecasts of solar insolation and wind speed. The core of the DSM is a fuzzy logic method which decides for efficient load shifting and/or load curtailment. The day-ahead forecasting errors, obtained by an artificial neural network technique, are considered not only in the DSM strategy but also in maintaining an operating reserve. The battery capacity degradation is calculated using the Rainflow counting algorithm to obtain a realistic battery model and estimate its lifetime. A typical household in South Australia (SA) is considered as a case study. Three different configurations (PV-BS, WT-BS, and PV-WT-BS) of the electricity supply system are optimized using the proposed method. It is found that the PV-WT-BS system is the best configuration that provides the lowest cost of electricity for both with and without applying the proposed DSM strategy. Comparison of the results of the best system configuration with an actual system in SA and two recently published articles indicates that the proposed method is very effective in lowering the electricity cost with zero-emission.

Since battery cost represents a substantial part of an electric vehicle’s (EV) total cost, the degradation of EV battery and how it is affected by being used in vehicle-to-grid (V2G) applications is a concern and the main argument against implementing V2G. Battery degradation is too complex in terms of non-linearity for practical optimization of V2G scheduling. This paper develops a mixed-integer linear programming (MILP) model to optimize the V2G scheduling of an EV, considering a detailed battery degradation model for calendar aging and cycle aging. In the developed model, calendar aging is affected by the state of charge, battery age, and temperature. The cycle aging is affected by temperature, C-rate, and the energy throughput of the battery. A case study is performed to minimize the annual operational cost of an EV with V2G capability for two different years of electricity cost and ambient temperature data. The results of the developed model are compared with four different cases: immediate charging, smart charging algorithms without V2G, V2G without degradation cost, and V2G with degradation cost as the objective function. It is shown that the developed V2G model achieves a slightly increased cycle aging due to usage in V2G. However, it reduces the overall scheduling cost of the EV by 48-88% compared to the immediate charging and by 10-73% compared to the smart charging. Furthermore, neglecting battery degradation in optimal V2G could lead to a 28% increase in scheduling cost.

In this paper, a robust Model Predictive Control (MPC) is proposed for the Gate-Controlled Series Capacitor (GCSC) to contribute to load frequency control. The proposed control model consists of a two-layer MPC in which a nominal MPC produces an initial control signal together with a prediction of system frequency response in a nominal system without uncertainty. An ancillary MPC then produces a control command for the actual system with uncertainty based on measurements and signals provided by the nominal system. The control signals are provided in such a way that the error in the frequency response of the actual system is minimized with respect to that of the nominal system. Optimization procedures are also conducted to attain optimal values of weighting coefficients associated with the inputs and outputs in the objective functions. The effectiveness of the proposed method to deal with uncertainties is compared with a conventional MPC, a scheme with proportional-integral controllers, and a system without GCSC. The method robustness is illustrated by case studies with load uncertainty and wind power fluctuations, and also uncertainty of parameters. The impact of the delay in communication links on the performance of the control scheme is also evaluated.

Due to the high price of fossil fuels, the increased carbon footprint in conventional generation units and the intermittent functionality of renewable units, alternative sources must contribute to the load frequency control (LFC) of the power system. To tackle the challenge, dealing with controllable loads, the ongoing study aims at efficient LFC in smart hybrid power systems. To achieve this goal, heat pumps (HPs) and electric vehicles (EVs) are selected as the most effective controllable loads to contribute to the LFC issue. In this regard, the EVs can be controlled in a bidirectional manner as known charging and discharging states under a smart structure. In addition, regarding the HPs, the power consumption is controllable. As the main task, this paper proposes a fractional order proportional integral differential (FOPID) controller for coordinated control of power consumption in HPs, the discharging state in EVs and automatic generation control (AGC). The parameters of the FOPID controllers are optimized simultaneously by the sine cosine algorithm (SCA), which is a new method for optimization problems. In the sequel, four scenarios, including step and random load changes, aggregated intermittent generated power from wind turbines, a random load change scenario and a sensitivity analysis scenario, are selected to demonstrate the efficiency of the proposed SCA-based FOPID controllers in a hybrid two-area power system.