Capacity Of Energy Storage Unit Research Articles

Minimizing Total Cost of Home Energy Consumption under Uncertainties

Along with the development of renewable energy sources, energy storage units are introduced to increase the stability and reliability of electricity production. The storage units can improve the efficiency of energy consumption for consumers as well. By smartly controlling home appliances, renewable energy sources and energy storage units, consumers can satisfy their energy demand with a minimum cost. However, the declined maximum capacity of energy storage units and the unstable power of electricity grid, due to randomly unexpected failures, can cause challenges for consumers’ energy plans. In this article, we develop a novel joint chance-constraint mixed-integer linear programming model to support consumers in finding the optimal energy plans for a minimum cost of energy consumption under the simultaneous impact of unexpected failures on energy storage units and electricity grid. A case study for a set of households in Nottingham, United Kingdom, is used to demonstrate the efficiency of the proposed model. Some interesting insights are achieved for home energy management under uncertainties.

Impact of Energy Storage on Renewable Energy Utilization: A Geometric Description

The high penetration of volatile renewable energy challenges power system operation. Energy storage units (ESUs) can shift the demand over time and compensate real-time discrepancy between generation and demand, and thus improve system operation flexibility and reduce renewable energy curtailment. This paper proposes two parametric optimization models to quantify how the power (MW) and energy (MWh) capacity of ESU would impact renewable energy utilization from two aspects: renewable energy curtailment and system flexibility for uncertainty mitigation. The two indicators are characterized as multivariate functions in the capacity parameters of ESUs. A severity ranking algorithm is suggested to pick up critical scenarios of fluctuation patterns from the uncertainty set; consequently, the proposed models come down to multi-parametric mixed-integer linear programs (mp-MILPs) which can be solved by a decomposition algorithm. The proposed method provides analytical expressions of the two indicators as functions in MW and MWh capacity. Such a characterization delivers abundant sensitivity information on the impact of ESU capacity parameters, and provides a powerful tool for visualization and useful reference for storage sizing. Case studies verify the effectiveness of the proposed method and demonstrate how to use the geometric information.

Energy storage allocation in wind integrated distribution networks: An MILP-Based approach

Due to the unpredictable nature of wind energy and non-coincidence between wind units output power and demand peak load, wind units is deemed as an unreliable source of energy. In order to compensate for the short-term fluctuation of wind energy, deployment of energy storage (ES) units in various types have been introduced as a viable solution. This paper develops a stochastic mathematical model for the optimal allocation of ES units in active distribution networks (ADNs) in order to reduce wind power spillage and load curtailment while managing congestion and voltages deviation. Nonlinearities of the original formulation are converted to linear equivalents and the final model lies within the computationally tractable mixed-integer linear programming (MILP) fashion. The IEEE 33-bus 12.66 kV radial distribution test system is utilized to illustrate the effectiveness of the proposed methodology. It was found that the rated power and capacity of ES units are depends on wind units' location and penetration level, in such a way that ES units are allocated near wind units to absorb excessive wind energy as much as possible. Furthermore, the results indicate ES units are useful for other purposes such as voltage management issue even if the wind units are not allocated.

Efficient Energy Management System Based on GA and Classical Boolean Approach

In this paper, two intelligent strategies for energy management unit for a home integrated with smart grid are proposed. The strategies are based on classical Boolean and genetic algorithm (GA). The objective is to optimize the cost saving for the end consumer. The price of energy varies by the hour depending on the load on the grid. The two strategies predict when and by how much the storage unit installed in the house should charge and release for 24 h of the day, satisfying the constraint that the load demand of the house at any particular hour should always be met. The strategies were tested by real time data collected by the Department of Energy for a typical house in the Chicago, Illinois region for the year 2013. Both the strategies achieve cost savings; however, it has been found that GA-based strategy results in higher cost saving. The impact of the capacity of the energy storage unit (ESU) on the cost saving has been analyzed for a GA strategy and cost saving obtained when the capacity of ESU is 1.5 times and 2 times the house hold load at any given hour is presented.