Effectiveness Of Energy Management System Research Articles

Assessment of Electricity Consumption Characteristic: Textile Factory Case Study

Currently, electrical energy tariffs are a crucial factor in the electricity market, as they significantly impact the decisions made by end users. They play a vital role in the effectiveness of energy management systems (EMS). Tariffs are not solely considered a fixed component of expenditure calculations. Instead, they are indirectly linked to the costs of power generation, electricity transmission, and electricity distribution, as well as other determinants such as government taxation. In certain regions, improper tariff calculation methodologies have resulted in substantial power losses, superluous investments, increased operating costs, and environmental pollution because of the underutilization of available renewable energy sources. This study examined the electrical energy consumption values and characteristics of an integrated textile factory. Additionally, analyses were conducted on the electricity tariffs published by the Energy Market Regulatory Authority (EMRA) Electricity Energy Market management, in order to decrease the electrical energy consumption costs of the textile factory. Based on the findings of the analyses, suggestions were put forward for regulating the electrical energy consumption characteristics and reducing the electrical energy consumption costs.

Determination of Effectiveness of Energy Management System in Buildings

Building Energy Management Systems (BEMS) are computer-based systems that aid in managing, controlling, and monitoring the building technical services and energy consumption by equipment used in the building. The effectiveness of BEMS is dependent upon numerous factors, among which the operational characteristics of the building and the BEMS control parameters also play an essential role. This research develops a user-driven simulation tool where users can input the building parameters and BEMS controls to determine the effectiveness of their BEMS. The simulation tool gives the user the flexibility to understand the potential energy savings by employing specific BEMS control and help in making intelligent decisions. The simulation is developed using Visual Basic Application (VBA) in Microsoft Excel, based on discrete-event Monte Carlo Simulation (MCS). The simulation works by initially calculating the energy required for space cooling and heating based on current building parameters input by the user in the model. Further, during the second simulation, the user selects all the BEMS controls and improved building envelope to determine the energy required for space cooling and heating during that case. The model compares the energy consumption from the first simulation and the second simulation. Then the simulation model will provide the rating of the effectiveness of BEMS on a continuous scale of 1 to 5 (1 being poor effectiveness and 5 being excellent effectiveness of BEMS). This work is intended to facilitate building owner/energy managers to analyze the building energy performance concerning the efficacy of their energy management system.

Economic Optimal Scheduling of Wind–Photovoltaic-Storage with Electric Vehicle Microgrid Based on Quantum Mayfly Algorithm

The effectiveness of energy management systems is a great concern for wind–photovoltaic-storage electric vehicle systems, which coordinate operation optimization and flexible scheduling with the power grid. In order to save system operation cost and reduce the energy waste caused by wind and light abandonment, a time-sharing scheduling strategy based on the state of charge (SOC) and flexible equipment is proposed, and a quantum mayfly algorithm (QMA) is innovatively designed to implement the strategy. Firstly, a scheduling strategy is produced according to the SOC of the battery and electric vehicle (EV), as well as the output power of wind–photovoltaic generation. In addition, the minimum objective function of the comprehensive operation cost is established by considering the cost of each unit’s operation and electricity market sale price. Secondly, QMA is creatively developed, including its optimization rule, whose performance evaluation is further carried out by comparisons with other typical bionics algorithms. The advantages of QMA in solving the low-power multivariable functions established in this paper are verified in the optimization results. Finally, using the empirical value of the power generation and loads collected in enterprise as the initial data, the mayfly algorithm (MA) and QMA are executed in MATLAB to solve the objective function. The scheduling results show that the time-sharing scheduling strategy can reduce the system’s cost by 60%, and the method decreases energy waste compared with ordinary scheduling methods, especially when using QMA to solve the function.