Amarasinghage Tharindu Dasun Perera

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.

Performance Analysis of Photovoltaic Thermal (PVT) Panels Considering Thermal Parameters

Solar PVT panels are getting popular for wider spectrum of applications for concurrent heat and power generation (CHP). These panels can provide the heating demand of buildings while generating electricity which becomes ideal for building applications of urban energy systems. Energy flow analysis of such panels and performance analysis of such systems becomes essential to design PVT systems matching with the operating conditions. A number of studies have used both theoretical and experimental methods to optimize PVT. However, this task is challenging due to interrelation of CHP production based on two different phenomena where classical optimization methods cannot be applied directly. Hence basic performance analysis considering primary design parameters plays a major role. In this study, a computational model is developed to evaluate sensitivity of design, operating and climatic parameters for a hybrid PVT system and to analyze the performances of PVT for five different design configurations. Five main configurations of the PVT system are considered based on the heat transfer fluid and the arrangements of glass and tedlar layers of PVT collector. This study presents comprehensive performance analysis conducted to evaluate the sensitivity of mass flow rate and working fluid temperature for the five different design configurations of PVT panels. Results show that glass-tedlar water collector performs better when compared to other configurations. Subsequently, the sensitivity of wind speed and solar irradiation is evaluated. The behavior of thermal and electrical efficiencies is analyzed at different wind speed and solar irradiation levels for a range of mass flow rates and working fluid temperatures. Important conclusions on the performance of PVT panels are given based on this detailed analysis.

Optimum Design of Standalone Hybrid Energy Systems Minimizing Waste of Renewable Energy

The importance of integrating renewable energy sources into standalone energy systems is highlighted in recent literature. Maintaining energy efficiency is challenging in designing such hybrid energy systems (HES) due to seasonal variation of renewable energy potential. This study evaluates the limitations in minimizing the losses in renewable energy generated mainly due to energy storage limitations and minimizing fuel consumption of the internal combustion generator (ICG). A standalone hybrid energy system with Solar PV (SPV), wind, battery bank and an ICG is modeled and optimized in this work. Levelized Energy Cost (LEC), Waste of Renewable Energy (WRE) and Fuel Consumption (FC) are taken as objective functions. Results highlight the importance of considering WRE as an objective function which increase the mix of energy sources that can help to increase the reliability of the system.

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.