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Refereed/Peer-reviewed This paper determines the optimal capacities of small wind turbine (SWT) and battery energy storage (BES) for a grid-connected household (GCH) with or without an electric vehicle (EV) to minimize the overall cost of electricity (COE). Rule-based home energy management systems (HEMSs) are developed for two different configurations of the GCH: (i) with only SWT, and (ii) with SWT and BES. For each configuration, the HEMSs are developed for two cases: with and without an EV in the premises of the GCH. Uncertainties are also included in the arrival time, departure time, and initial state of charge (at arrival) of the EV. The above technique is then applied to a typical household in South Australia (SA) using the yearly load profile of the household and actual yearly wind speed data at an interval of one hour. To investigate the effects of stochastic nature of household load, EV, and wind power generation on various results, the optimization process is repeated using 10-year of actual wind speed data and probabilistic load and EV uncertainties. The results of several sensitivity analyses of various system parameters are presented. It has been found that the SWT can effectively decrease the COE of the household for both cases (with and without an EV). However, the current price of battery may not be in favor of further reducing the COE of the household.

Refereed/Peer-reviewed This article proposes a two-stage optimization algorithm to effectively determine the system configuration at one stage, as well as the capacity of components at the other stage in the middle of the former. This algorithm fits in best with the hybrid systems with more possible types of components. The studied system, in this article, includes diesel generators, wind turbines, photovoltaic arrays, and tidal generators as the power generation components, as well as battery banks and flywheels as the energy storage components. It also includes fuel cells and electrolyzers that either work as batteries or generate electricity in the presence of biomass. When the number of components (decision variables) increases, it becomes difficult to find an optimal solution by the conventional methods. Therefore, in this study, a two-stage multiobjective optimization algorithm is applied to design a cost effective and environmental friendly ac minigrid hybrid power system. In each iteration of the proposed algorithm, first, renewable energy sources and energy storage components are selected to form a hybrid power system along with the diesel generator. Then, the capacities of the components are optimized based on the two objective functions, including levelized cost of electricity and emissions. The optimization model uses real annual data in hourly time intervals for the load, solar insolation, ambient temperature, and wind speed. It is found that the proposed algorithm decreases the susceptibility of the solutions to multiple runs compared with the conventional algorithms and achieves a better optimized power system design.

Refereed/Peer-reviewed This paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) with novel rule-based energy management systems (EMSs) under flat and time-of-use (ToU) tariffs. Four schemes are investigated based on the combinations of flat and ToU tariffs for buying and selling the electricity: (1) Flat-Flat, (2) ToU-Flat, (3) Flat-ToU, and (4) ToU-ToU. For each scheme, two configurations are evaluated: (i) PV only, and (ii) PV-BES. The optimization of the grid-connected household is evaluated based on one-year realistic data. An uncertainty analysis is presented based on the variations of insolation, temperature, and load. Sensitivity analyses are implemented based on the average daily load, the grid constraint, and the costs of PV and BES. The operational analyses for 48 h in summer and winter are carried out to evaluate the dynamic performance of the systems for high and low solar insolation. The effectiveness of the proposed model is verified by comparing the results with that of common EMS based on the net metering scheme. It is found that the COE of the proposed EMS for a PV-BES house with ToU-Flat scheme (as the best option) is 2 ¢/kWh lower than that of the net metering scheme.

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. Refereed/Peer-reviewed

This article 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. The 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-mentioned technique is applied to two system configurations—household with a solar PV and a BES. The uncertainty analysis is implemented using ten years of 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. The sensitivity analysis is 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. Refereed/Peer-reviewed

This study investigates the impact of incentive-based demand response on the optimal economic sizing of hybrid power systems for a remote area in South Australia. The hybrid power systems are modelled as AC-coupled system with various power generation and energy storage systems including diesel generators, solar photovoltaics, wind turbines, battery storages and flywheels. Operating reserve requirements are introduced to ensure a specified reliability with variable renewable energy generation and consumer loads. Incentive-based demand response is introduced to allow a reduction in customer loads, up to a maximum value, during peak load events. Customers receive a financial benefit as an incentive for the total demand response energy reduction. Active power operation of four different power system configurations is modelled over one year in hourly time steps. The study uses real data for customer demand, wind speed, solar insolation and ambient temperature profiles in a specific location. The hybrid power systems with demand response are optimised to minimise the system net present cost in project lifetime (20 years) using a particle swarm optimisation algorithm. Sensitivity analysis of levellised cost of energy for various values of the maximum demand response power and the incentive payments are also carried out. Refereed/Peer-reviewed

This paper investigates a comparative study on optimal sizing of fuel cell (FC) and battery energy storage (BES) systems coupled with solar photovoltaic (PV) and wind turbine (WT) for a remote area power system. Two system configurations: (1) PV-WT-BES and (2) PV-WT-FC are optimally sized based on actual annual data of wind, solar radiation, ambient temperature and load profile of a remote area community in South Australia. The costs of generation and storage units are considered based on real Australian market prices. The levelized cost of electricity (LCOE) is used as the objective function and appropriate optimization constraints are considered for each system. It is found that the BES technology is more economic than FC system for power system design in Australian remote areas. However, the PV-WT-FC system results in lower dumped energy. Sensitivity analysis is conducted to investigate the effects of the FC system price and its efficiency on LCOE and dumped energy of the system. The option of adding reactor/reformer to the FC system to produce hydrogen from the human waste is also studied in terms of optimal capacities, economic and dumped energy. Refereed/Peer-reviewed

This paper presents a two-stage optimization technique in sizing various components of standalone hybrid electricity systems with time-of-use (ToU) incentive demand response program. In the first stage of optimization, the minimum levelized cost of electricity (LCOE) is determined without using demand response. The result of the first stage (LCOE) is used as a base rate to develop a ToU demand response for incentive payment in the second stage of optimization. In developing the incentive payment, three periods of a day (off-peak, shoulder, and peak) with different payment rates of electricity are considered. Five different standalone system configurations are developed using various combinations of diesel generators, wind generators, solar photovoltaics, battery energy storages, and flywheels. The proposed two-stage optimization technique is then applied to all five configurations of a remote area South Australian community. Real yearly data of electricity consumption, solar radiation, wind speed, and air temperature, as well as real market price of the components are used in the optimization. It has been found that the hybrid standalone system consisting of diesel, solar, wind, and battery has the minimum overall cost of electricity. Refereed/Peer-reviewed

This paper investigates the capacity optimization of three different renewable-based systems for standalone households in South Australia. Solar photovoltaic (PV), wind turbine (WT) and battery energy storage (BES) are the main components for such systems. Three selected configurations of the system are: PV with battery, WT with battery, and PV-WT with battery. Optimal capacity of each component is determined through an optimization process by considering the system net present cost as an objective function. The optimization is based on the one-year hourly real data of load consumption, wind speed, solar insolation and air temperature of a remote area. Capacities of the system components are first optimized for a case of uninterruptible supply of the load consumption. It is demonstrated that such a system results in high cost and large capacity of energy storage. Two strategies are then proposed to reduce the components capacities and hence the total system cost. The first strategy is to reduce the peak demands manually. In the second strategy, loss of power supply probability is used as a reliability index to reduce the capacity of the components and the total cost substantially. Sensitivity analysis are also carried out based on the component's capital cost and daily average load consumption for the optimum configuration. Annual operational results are presented and discussed for the optimal configuration. Refereed/Peer-reviewed

This paper investigates the optimal capacities of solar photovoltaic (PV) and battery energy storage (BES) for grid-connected households in South Australia. The optimisation is based on the net present cost of the electricity in a 20-yearlifespan. Real data of load pattern, solar insolation and temperature at hourly interval as well as the electricity rates(retail price and feed-in-tariff) of South Australia are used in this study. Three different configurations with appropriate rule based home energy management are proposed. Optimal capacities of the PV and BES are found in each configuration based on four different scenarios of PV capacity limited by the availability of roof size. The maximum export power limitations for South Australian households is considered for the optimization. The cost of electricity (¢/kWh) is selected as an index for comparison between the proposed systems. It is found that the proposed optimal system can be more beneficial for the households with lower electricity consumption. The study examines the sensitivity of the results to the average load consumption and costs of PV and battery. Annual load, PV power, battery charging/discharging, dumped energy, state-of charge of battery as well as the grid export/import power are shown and discussed. The optimal system is compared with azero net energy home in South Australia. A general guideline is demonstrated for the customers to purchase the optimal capacities of PV and BES. Refereed/Peer-reviewed