Vahid M. Nik

Optimum design and control of grid integrated electrical hubs considering lifecycle cost and emission

Grid connected renewable energy systems are becoming popular due to reasons such as rapid escalation of energy prices, depletion of fossil fuel resources and pollutant emitted by conventional energy sources. Therefore, technologies for incorporating renewable energy technologies into the existing electricity grid needs to be researched more considering the changes in grid architecture. This study presents a novel method for optimum design and control of an Electric-Hub (EH) which consist of Solar PV panels, wind turbines, battery bank operating in a grid (low voltage) integrated mode. This study reports the simulation based optimization algorithm developed to obtain optimum system configuration and operation strategy considering two conflicting objectives; i.e. Levelized Energy Cost (LEC) and Leveliyed CO2 emission (LCO2). A detail energy flow model is developed to evaluate energy flow through wind turbines and SPV panels on hourly basis. Interaction with the battery bank and the Low-Voltage Grid (LVG) is determined using an expert system. Operating state of the system is determined based on renewable energy generation, Cost of Electricity (COE) in the LVG, state of charge of the battery bank. Subsequently, operating states of the expert system and configuration of the EH; i.e. type and capacity of SPV panels, wind turbines and battery bank is optimized using steady state ε-multi objective optimization technique. Seven Pareto solutions are selected at the end and analyzed the system configuration and control strategy.

Sensitivity of the dispatch strategy in designing grid integrated hybrid energy systems

Integrating renewable energy technologies based on solar PV (SPV) and wind energy in the energy system is challenging due to time dependence of the energy potential for these energy sources. Grid integrated hybrid energy systems combining SPV panels, wind turbines, battery bank and internal combustion generators (ICG) can be used in this regard specially for distributed generation. Energy-economic dispatch strategy plays a vital role in managing the energy flow of the system. However, it is difficult to design such energy system due to complexity of the energy flow because of the changes in electricity demand, solar and wind energy potential. This study evaluates the sensitivity of dispatch strategy on optimum system configuration considering Levelized Energy Cost (LEC) and Grid Integration Level (GI). Two existing dispatch strategies i.e. cycle charging (CC) and modified cycle charging (MCC) dispatch strategies are used for the analysis based on Pareto multi objective optimization of LEC and GI. Pareto-fronts obtained considering both approaches were subsequently compared for different grid curtailments. The results show that a notable difference in LEC and system configuration is observed for two different approaches with the increase of grid interactions.

Design Optimization of Electrical Hubs Using Hybrid Evolutionary Algorithm

Integration of non-dispatchable renewable energy sources such as wind and solar into the grid is challenging due to the stochastic nature of energy sources. Hence, electrical hubs (EH) and virtual power plants that combine non-dispatchable energy sources, energy storage and dispatchable energy sources such as internal combustion generators and micro gas turbines are getting popular. However, designing such energy systems considering the electricity demand of a neighborhood, curtailments for grid interactions and real time pricing (RTP) of the main utility grid (MUG) is a difficult exercise. Seasonal and hourly variation of electricity demand, potential for each nondispatchable energy source and RTP of MUG needs to be considered when designing the energy system. Representation of dispatch strategy plays a major role in this process where simultaneous optimization of system design and dispatch strategy is required. This study presents a bi-level dispatch strategy based on reinforced learning for simultaneous optimization of system design and operation strategy of an EH. Artificial Neural Network (ANN) was combined with a finite state controller to obtain the operating state of the system. Pareto optimization is conducted considering, lifecycle cost and system autonomy to obtain optimum system design using evolutionary algorithm.