Optimal Energy Management of V2B with RES and ESS for Peak Load Minimization

In order to decrease fuel consumption and greenhouse gas emissions, electric vehicles (EVs) are being widely adopted as a future transportation system. Accordingly, increasing the number of EVs will mean battery charging will have a significant impact on the power grid. In order to manage EV charging, an intelligent charging strategy is required to prevent the power grid from overloading. Therefore, we propose an optimal energy management algorithm (OEMA) to minimize peak load on a university campus consisting of an educational building with laboratories, a smart parking lot, EVs, photovoltaic (PV) panels and an energy storage system (ESS). Communication networks are used to connect all the system components to a university energy management system (UEMS). The proposed OEMA algorithm coordinates EV charging/discharging so as to reduce the peak load of the building’s power consumption by considering the real-time price (RTP). We also develop a priority determination method for the time allocation of the optimal charging algorithm. Priority is determined by arrival time, departure time, state-of-charge (SOC), battery capacity and trip distance. The performance of the proposed algorithm is evaluated in terms of charging cost and peak load under the real environment of the university engineering building.

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Optimal Energy Management of EV Parking Lots Under Peak Load Reduction Based DR Programs Considering Uncertainty

IEEE Transactions on Sustainable Energy ◽

10.1109/tste.2018.2859186 ◽

2019 ◽

Vol 10 (3) ◽

pp. 1034-1043 ◽

Cited By ~ 6

Author(s):

Ibrahim Sengor ◽

Ozan Erdinc ◽

Baris Yener ◽

Akin Tascikaraoglu ◽

Joao P. S. Catalao

Keyword(s):

Energy Management ◽

Peak Load ◽

Load Reduction ◽

Parking Lots ◽

Optimal Energy ◽

Optimal Energy Management

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Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response

Energies ◽

10.3390/en12214142 ◽

2019 ◽

Vol 12 (21) ◽

pp. 4142 ◽

Cited By ~ 4

Author(s):

Hyung-Joon Kim ◽

Mun-Kyeom Kim

Keyword(s):

Energy Management ◽

Demand Response ◽

Operating Cost ◽

Peak Load ◽

Management Approach ◽

Solution Quality ◽

Peak Period ◽

Multi Objective ◽

Optimal Energy ◽

Optimal Energy Management

This paper proposes an optimal energy management approach for a grid-connected microgrid (MG) by considering the demand response (DR). The multi-objective optimization framework involves minimizing the operating cost and maximizing the utility benefit. The proposed approach combines confidence-based velocity-controlled particle swarm optimization (CVCPSO) (i.e., PSO with an added confidence term and modified inertia weight and acceleration parameters), with a fuzzy-clustering technique to find the best compromise operating solution for the MG operator. Furthermore, a confidence-based incentive DR (CBIDR) strategy was adopted, which pays different incentives in different periods to attract more DR participants during the peak period and thus ensure the reliability of the MG under the peak load. In addition, the peak load shaving factor (PLSF) was employed to show that the reliability of the peak load had improved. The applicability and effectiveness of the proposed approach were verified by conducting simulations at two different scales of MG test systems. The results confirm that the proposed approach not only enhances the MG system peak load reliability, but also facilitates economical operation with better performance in terms of solution quality and diversity.

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Optimal Energy Management and Sizing of a Community Smart Microgrid Using Demand Side Management with Load Uncertainty

ECTI Transactions on Computer and Information Technology (ECTI-CIT) ◽

10.37936/ecti-cit.2021152.240491 ◽

2021 ◽

Vol 15 (2) ◽

pp. 186-197

Author(s):

MANEESH KUMAR ◽

Barjeev Tyagi

Keyword(s):

Energy Management ◽

Peak Load ◽

Demand Side Management ◽

Small Scale ◽

Optimal Size ◽

Demand Side ◽

Small Community ◽

Capital Costs ◽

Optimal Energy ◽

Optimal Energy Management

This paper presents an optimal energy management and sizing of a smart community microgrid (MG) with the uncertainty in load demand. An isolated small scale microgrid is considered with no access to the main supply grid. For simplicity, a small community of 15 houses located in a remote area is considered, and the loads are divided into controllable and uncontrollable categories. Demand side management (DSM) is being utilized to produce a feasible alteration to the controllable part of the load. The Overall problem is formulated to fix the optimal size of distributed generations (DGs) used in the MG by using a heuristic approach to minimize the net cost-based optimization problem. This cost includes initial capital costs, operation, and maintenance costs, and other running costs associated with MG. The optimization is completed in two parts. The first part of optimization is done without DSM implementation, and second part optimization is done on the modified system peak load after DSM implementation. Quantitative results on a numerical case study give an optimal number of distributed generation (DGs), their corresponding optimal ratings, optimal cost value, reduction in carbon footprint, and annual cost savings in the form of CO2 emission tax.

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