Abstract

Existing energy systems face problems such as depleting fossil fuels, rising energy prices and greenhouse gas (GHG) emissions which seriously affect the comfort and affordability of energy for large-sized commercial customers. These problems may be mitigated by the optimal scheduling of distributed generators (DGs) and demand response (DR) policies in the distribution system. The focus of this paper is to propose an energy management system (EMS) strategy for an institutional microgrid (μG) to reduce its operational cost and increase its self-consumption from green DGs. For this purpose, a real-time university campus has been considered that is currently feeding its load from the national grid only. However, under the proposed scenario, it contains building owned solar photovoltaic (PV) panels as non-dispatchable DG and diesel generator as dispatchable DG along with the energy storage system (ESS) to cope up with the intermittency of solar irradiance and high operational cost of grid energy. The resulting linear mathematical problem has been mapped in mixed-integer linear programming (MILP) and simulated in MATLAB. Simulations show that the proposed EMS model reduces the cost of grid electricity by 35% and 29% for summer and winter seasons respectively, while per day reductions in GHG emissions are 750.46 kg and 730.68 kg for the respective seasons. The effect of a half-sized PV installation on energy consumption cost and carbon emissions is also observed. Significant economic and environmental benefits as compared to the existing case are enticing to the campus owners to invest in DG and large-scale energy storage installation.

Highlights

  • Energy systems have been facing problems such as inflating consumption cost, greenhouse gas (GHG) emission, network overloading, etc

  • As the sizing of distributed generation is not being addressed in our work, a 2 MW of onsite solar PV installation is assumed for detailed technoeconomic analysis

  • time of use (TOU) tariff was considered and energy storage system (ESS) was used as demand response (DR) flexible system which can be intelligently charged and discharged during different times to achieve cost minimization objective without compromising its life span

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Summary

CONSTANTS AND VARIABLES

Value of BSOC at time t Minimum level of BSOC (%) Initial state of BSOC (%) Maximum level of BSOC (%) Net cost of energy ($) Storage degradation cost ($). Gradient power of storage system (kW) Charging power of storage system (kW) Discharging power of storage system (kW) Maximum charging power of storage system (kW) Maximum discharging power of storage system (kW) Load demand of prosumer (kW). Output power of diesel generator Grid power (kW) Maximum exchange power limit of grid (kW) Minimum exchange power limit of grid (kW) Time interval (Hour) Capacity of diesel generator Storage charging/discharging integers Electricity rates ($/kWh) Mean of solar irradiance Standard deviation of solar irradiance Solar panel efficiency Solar panel area Fuel curve intercept of diesel generator Fuel curve slope of diesel generator

INTRODUCTION
RELATED WORK
LIMITATIONS OF GRID AND DIESEL GENERATOR
PROBABILISTIC PV MODEL
LEVELIZED COST OF ENERGY
RESULTS AND DISCUSSION
CONCLUSION
Full Text
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