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This paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) for a grid‐connected house based on an energy‐sharing mechanism. The grid‐connected house, also mentioned as house 1 where it is relevant, shares electricity with house 2 under a mutually agreed fixed energy price. The objective is to minimize the cost of electricity (COE) for house 1 while decreasing the electricity cost of house 2. Practical factors such as real data for solar insolation, electricity consumption, grid constraint, ambient temperature, electricity rate, and battery degradation are considered based on actual data. The developed methodology is examined by taking the actual load data of two houses in South Australia. Different scenarios of contract years between the houses are investigated to make it more practical in real life. Sensitivity analyses are conducted for the sharing of energy between the houses and by changing parameters like export power limitation, load of houses, and costs of PV and BES. Likewise, operational analysis is done for two days of summer and winter. It is found that when energy sharing is applied, the optimal design of the PV‐BES system will achieve lower COE for both houses. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance and suitability of four different metaheuristic algorithms for optimal sizing of standalone microgrids in remote area. The studied metaheuristic algorithms are particle swarm optimization, differential evolution, water cycle algorithm and grey wolf optimization. These algorithms are applied to optimize the capacity of diesel generator, fuel tank, solar photovoltaic, wind turbine, and battery energy storage in four different AC-coupled standalone microgrids for a remote area community in South Australia. The objective function is selected as the net present value of electricity over a 20-year lifetime. The optimisation study is conducted based on the real data of annual load consumption, ambient temperature, solar insolation, and wind speed of the site. Capital, replacement, and maintenance costs of components in Australian market are incorporated for the economic analysis. An operating power reserve is maintained based on the static and dynamic reserve concepts. Uncertainty analysis based on 10-year real data of renewable energies and load consumption is conducted. Sensitivity analysis is provided for variations of the battery price and capacity. The performance of the applied algorithms is evaluated by comparing the economic and operational results, as well as the computational time and optimization convergence. It is found that differential evolution algorithm is unreliable for optimal sizing problem of the studied standalone microgrids.. [ABSTRACT FROM AUTHOR]

This article develops practical resiliency-oriented optimal planning models for a grid-connected system with renewable resources and battery energy storage. A long-term (ten-year) operation is conducted by incorporating grid outages, operating reserve, detailed models of electricity prices, and components’ capacity degradations and costs. The number of grid outages per year as well as the days, starting time, and duration of the outages are adopted using probability distribution functions. Interest and escalation rates are updated in each year of operation to update the retail price, feed-in-tariff, and penalty rate of the electricity provider for the unsupplied power during grid outages. A novel operation strategy is developed for islanded and grid-connected modes of operation. The renewable-battery system is optimized for two cases: 1) a resilient system without load interruption; and 2) a resiliency-constrained system with partial load interruption. A case study in South Australia is conducted by incorporating the load profile of an educational campus and the actual weather data for a project lifetime of ten years. The resiliency-oriented optimal planning model is compared to the same system optimized by short-term data. It is found that the resiliency-constrained system achieves lower cost, battery capacity, and dumped energy with limited unsupplied energy compared to the resilient system, which has zero unsupplied energy. The proposed long-term resilient system is found to need 2.2-MWh battery capacity to efficiently supply 17.58-MWh electricity during the grid outages. The cost of electricity for the proposed resilient system is 23.77 ¢/kWh. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the operation of various optimised AC‐coupled hybrid power systems for an isolated community in South Australia. In addition to diesel generators, various combinations of distributed generators and storage technologies are considered to form different hybrid power systems. Optimal design of the hybrid power systems is implemented using particle swarm optimisation (PSO) algorithm. The performance of the PSO in optimizing the above hybrid systems is discussed in detail. Whilst meeting the power system operational constraints, the minimum levelised cost of electricity (LCOE) of all systems, with various combinations of system components, are calculated. Simulation results obtained for each optimized system are carefully described and analysed. The annual operations of various components in each system are then compared and discussed in detail. A sensitivity analysis by varying the cost of wind turbines and the photovoltaic is also conducted for all hybrid systems. It has been found that the inclusion of energy storage systems (battery and flywheel) can reduce the LCOE and CO2 emission significantly compared to the inclusion of renewable generations (solar and wind) alone. The diesel‐wind‐solar‐battery system is the eco‐friendliest with an annual CO2 emission of 451 t compared to the reference diesel system (1064 t). In addition, the hybrid system consisting of diesel, wind, solar, battery and flywheel is found to be the most economic with a LCOE of 58.94 ¢/kWh compared to the diesel system with a LCOE of 67.17 ¢/kWh. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study determines the optimal capacity of various components of an AC mini-grid hybrid power system in a remote area of South Australia. A range of generation and storage technologies, such as diesel generators, wind turbines, solar photovoltaic arrays, battery banks, and flywheels are considered. A minimum system operating reserve is maintained to ensure a certain degree of reliability. Four different configurations of hybrid power systems are analysed in hourly time-steps, over a year, using real data for system load, solar insolation, ambient temperature, and wind speed. Particle swarm optimisation approach is used to optimise the capacity of various components of each hybrid system to obtain the minimum net present cost (NPC) over a 20-year system lifespan. Suitable capital, operating, and maintenance costs in Australian context are considered in evaluating the system. Various simulation results, such as annual energy balances, NPCs, levelised cost of energy and cash flow analysis obtained for each optimised system are carefully analysed and discussed. The modelling and results confirms the feasibility and economical replacement of the diesel power system with those of hybrid power systems which use renewable energy supply. [ABSTRACT FROM AUTHOR]