Peak shaving of utility grid power is an important application, which benefits both grid operators and end users. In this article, an optimal rule-based peak shaving control strategy with dynamic demand and feed-in limits is proposed for grid-connected photovoltaic (PV) systems with battery energy storage systems. A method to determine demand and feed-in limits depending on the day-ahead predictions of load demand and PV power profiles is developed. Furthermore, an optimal rule-based control strategy that determines day-ahead charge/discharge schedules of battery for peak shaving of utility grid power is proposed. The rules are formulated such that the peak utility grid demand and feed-in powers are limited to the corresponding demand and feed-in limits of the day, respectively, while ensuring that the state-of-charge (SoC) of the battery at the end of the day is the same as the SoC of the start of the day. The optimal inputs required for applying the proposed rule-based control strategy are determined using a genetic algorithm for minimizing peak energy drawn from the utility grid. The proposed control algorithm is tested for various PV power and load demand profiles using MATLAB.

This article presents an optimal demand response (DR) control for a residential photovoltaic (PV) storage system using energy pricing limits. Unlike the existing literature, both energy buying and selling prices are considered in DR application for reducing the energy cost of the system. A method for determining buying price and selling price limits of the day using day-ahead predictions of energy price, load demand, and PV power profiles is proposed. To further reduce the energy cost of the system subbuying price limit is considered. A rule-based DR control for minimizing the energy cost of the system is proposed. The rules are formulated such that energy buying price and selling prices are limited to their corresponding price limits of the day with the flexible day-to-day management of battery energy storage system. For obtaining optimal results with rule-based control, required control inputs are determined optimally using genetic algorithm. The proposed method is tested in MATLAB. It is observed that energy cost savings of 34.09% and 5.4% are obtained on the day of more PV energy availability and less PV energy availability, respectively, based on the day-ahead operation. Further, the impact of uncertainties on the proposed DR control is discussed for real-time operation of the system.

This paper proposes a rule-based peak shaving method using master-slave level optimization and presents its impact on an isolated diesel generator (DG) supplied microgrid. The DG is connected to low voltage (LV) ac bus through a back-to-back converter. This DG supplies power to a modified CIGRE residential distribution system which consists of residential loads and renewable energy sources (RESs). In such a system the fuel consumption of DG not only depends on the LV ac bus power requirement but also on its rated power. The rated power of DG can be reduced through the peak shaving application. In this scenario, a battery energy storage system (BESS) is connected at dc bus of the back-to-back converter for the peak shaving of DG power. The rule-based peak shaving method is proposed for determining BESS charge/discharge schedules considering the day-ahead LV ac bus powers over a day as inputs. For that firstly the LV ac bus powers are optimally obtained using a slave level optimization. In order to obtain the optimal peak DG power a master level optimization is used which determines the optimal control inputs for the rule-based control. The proposed peak shaving control method is tested in MATLAB.

In this article, the optimal sizing of hybrid solar photovoltaic and battery energy storage systems is evaluated with respect to rooftop space and feed-in tariff rates. The battery scheduling is performed using a proposed rule-based energy management strategy. The rules are formulated based on the demand limit, PV export power limit, and state of charge of the battery. Furthermore, optimization modeling with initial choices of parameters and constraints in terms of solar photovoltaic and battery energy storage capabilities is developed to minimize the total net present cost. The hourly values of solar irradiance, air temperature, electrical loads, and electricity rates are considered the inputs of the optimization process. The optimization results are achieved using particle swarm optimization and validated through an uncertainty analysis. It is observed that an optimal photovoltaic and battery energy storage system can reduce the cost of electricity by 12.33%, including the sale of 5944.029 kWh of electricity to the grid. Furthermore, energy consumption, peak demand, and greenhouse gas emissions are reduced by 13.71%, 5.85%, and 62.59%, respectively. A comprehensive analysis between the variable and fixed data for the load, energy from PV, batteries, and the grid, and costs demonstrates that the optimal sizing of photovoltaic and battery energy storage systems with the best mix of energy from PV, batteries, and the grid provides the optimal solution for the proposed configuration.

In modern power systems, it is important to compensate net load forecast errors which are caused due to variability and uncertainty of load and renewable energy source powers for improving the stability and reliability. Super capacitor (SC) is a well suited component for compensating forecast error due to its power and energy density characteristics. In this article, a novel method is proposed for net load forecast error compensation through SC application. The proposed method is simple and general which can be used at industry scale while implementing the energy management applications. In order to test the performance of the proposed method, a grid-connected photovoltaic storage system is considered. The forecast error in load/PV powers is compensated while limiting both grid demand and feed-in powers to their optimal limits. Battery energy storage system charge/discharge schedules are controlled by a rule-based peak shaving method to minimize the day-ahead average peak demand of the system. For controlling SC charge/discharge schedules, the proposed net load forecast error compensation method is used. It is observed that the peak demand and feed-in powers are reduced significantly with the proposed method as compared with the case when there is no net load forecast error compensation.