Existing electricity supply systems face several challenges, including increasing energy prices with greenhouse gas (GHG) emissions and fossil fuel depletion. These issues have a significant impact on all power system stakeholders, including customers/prosumers, utilities, and microgrid operators. Renewable energy incorporation and different energy managing strategies such as demand-side management (DSM), demand response (DR), and others may help to overcome these limitations. Campus microgrids are among the largest energy consumers in the United States, with high energy expenditures. This article presents a new energy management (EMS) system for a university campus microgrid with onsite solar PV and ESS that operates in a grid exchange scenario. The suggested EMS not only lowers power consumption costs by prolonging storage life; however, it also guarantees grid stability through limiting and shifting loads using price-based and incentive-based demand response methods. ESS is utilized as a stand-by energy reserve to maintain the microgrid system stability and to assist the utility network in the event of a power outage. In MATLAB, a quadratic approach is used to solve the proposed framework. According to the findings, the suggested EMS decreases the prosumer's operating cost and increasing self-consumption, minimizes peak load from the national grid, and encourages campus stakeholders and energy controllers to engage in large-scale ESS installations and distributed generation (DG).
Background: Current energy systems face multiple problems related to inflation in the energy prices, reduction of fossil fuels, and greenhouse gas emissions in disturbing the comfort zone of energy consumers and affordability of power for large commercial customers. This kind of problem can be alleviated with the help of optimal planning of Demand Response policies and with distributed generators in the distribution system. The objective of this article is to give a strategic proposition of an energy management system for a campus microgrid (µG) to minimize the operating costs and to increase the self-consuming energy of green DGs. To this end, a real-time-based campus is considered that is currently providing its loads from the utility grid only. Yet, according to the proposed given scenario, it contains the solar panels and wind turbine as a non-dispatchable DG while a diesel generator is considered as a dispatchable DG. It also incorporates the energy storage system with the optimal sizing of BESS to tackle with multiple disturbances that arise from solar radiations. Results: The resultant problem of linear mathematics has been simulated and plotted in MATLAB with mixed-integer linear programming. Simulation results show that the proposed given model of EMS minimizes the grid electricity costs by 31% in case of summer and 38% in case of winter respectively, while the reduction of GHG emissions per day is 780.68 and 730.46 kg for the corresponding summer and winter seasons. The general effect of a medium-sized solar PV installation on carbon emissions and energy consumption costs is also observed. Conclusion: The substantial environmental and economic benefits compared to the present case prompt campus owners to put investment in the DGs and to install large-scale energy storage.
The automated gain control (AGC) units as well as other non AGC equipment may be utilized in real-time power transmitting (RTPD) to coordinate the operations (RTD). In order to guarantee high-probability system security and to save operating costs, it is essential to correctly define the probable Wind Energy Forecast (WPFE) mistakes in RTD. The Cauchy Distribution (CD) is the perfect match for the "leptokurtic" characteristic of WPFE small-scale distributions, following previous research and our onsite testing. In this study, the CD represents WPFE, which is suggested to provide a chance-controlled real-time dispatch (CCRTD) paradigm (Chance-Constrained Randomization). The suggested CCRTD Model may be analytically converted to the "Convex Optimisation Problem," which takes into consideration the dependency of the wind farm outputs because of the stability and attractive mathematical features of the CD. The inclusion of a refined control method that may also be used in combination with AGC systems is an additional aspect of the suggested model. This technique, when combined with the WPFE RTD Stage, allows the CCRTD to respond to the higher ramping power requirements as well as power variations on WPFE-generated transmittal lines. The proposed technique was shown to be trustworthy and efficient in numerical testing. It is nevertheless extremely effective as well as suitable for usage.
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