Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid

A paradigm change in energy system design tools, energy market, and energy policy is required to attain the target levels in renewable energy integration and in minimizing pollutant emissions in power generation. Integrating non-dispatchable renewable energy sources such as solar and wind energy is vital in this context. Distributed generation has been identified as a promising method to integrate Solar PV (SPV) and wind energy into grid in recent literature. Distributed generation using grid-tied electrical hubs, which consist of Internal Combustion Generator (ICG), non-dispatchable energy sources (i.e., wind turbines and SPV panels) and energy storage for providing the electricity demand in Sri Lanka is considered in this study. A novel dispatch strategy is introduced to address the limitations in the existing methods in optimizing grid-integrated electrical hubs considering real time pricing of the electricity grid and curtailments in grid integration. Multi-objective optimization is conducted for the system design considering grid integration level and Levelized Energy Cost (LEC) as objective functions to evaluate the potential of electrical hubs to integrate SPV and wind energy. The sensitivity of grid curtailments, energy market, price of wind turbines and SPV panels on Pareto front is evaluated subsequently. Results from the Pareto analysis demonstrate the potential of electrical hubs to cover more than 60% of the annual electricity demand from SPV and wind energy considering stringent grid curtailments. Such a share from SPV and wind energy is quite significant when compared to direct grid integration of non-dispatchable renewable energy technologies.

A multi criterion analysis for renewable energy integration process of a standalone hybrid energy system with internal combustion generator

Amarasinghage Tharindu Dasun Perera

Integrating renewable energy into standalone Internal Combustion Generator (ICG) systems is an economical and eco-friendly option. However, previous studies demonstrate the difficulties in replacing the ICGs completely by using Solar PV (SPV) and wind energy with a dispatchable energy storage. This makes it interesting to analyze the limitations in integrating the SPV and wind energy into Hybrid Energy System. A multi criterion analysis is presented in this study, considering Levelized Energy Cost, Loss of Load Probability, and Fuel Consumption varying the scale of the ICG capacity to attain aforementioned objective. Changes in the system design with the integration of the SPV and wind energy were analyzed using Pareto multi-objective optimization considering Renewable Energy Capacity as an objective function. Sensitivity of the ICG capacity on optimum Renewable Energy Technology, role of the ICG in improving system reliability, etc., were subsequently analyzed. The results depict that the ICG capacity notably influence to the balance between wind and SPV capacity. An increase in the ICG capacity does increase the contribution from dispatchable energy source in most of the scenarios. Furthermore, it facilitates to amalgamate highly fluctuating renewable energy sources at a relatively low cost. This makes it inevitable to replace ICG with non-dispatchable renewable energy sources and energy storage.

Amer Inst Physics2015

Evaluating the sensitivity of grid integration level for a multi energy hubs

Dasaraden Mauree, Vahid Moussavi Nik, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini

Renewable energy integration into multi energy grids (MEG) is gradually getting popular due to the rapid depletion of fossil fuel resources and the global concern on GHG emissions through fossil fuel based energy conversion technologies. However, seasonal changes in solar and wind energy potentials make it challenging to increase the renewable energy capacity especially for urban applications where demand for energy services show a complex variation. According to recent literature, connecting multi energy hubs (MEHs) to a MEG can be a promising method to address the aforementioned challenge. However, a quantitative analysis of the improvement of system autonomy and renewable energy integration through multi energy hubs is missing. In this work, Homer microgrid design tool is used to analyze the sensitivity of the grid integration level to the renewable energy integration process of a multi energy hub. Heating and electricity demand of the MEH and the energy flow through Solar PV panels, wind turbines, boiler and battery bank are evaluated on hourly basis. Homer, microgrid simulation tool is used to evaluate the sensitivity of electricity cost to renewable energy capacity and energy storage.

LESO-PB, EPFL2015

Design optimization of electrical hubs using hybrid evolutionary algorithm

Dasaraden Mauree, Vahid Moussavi Nik, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini

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.

Amer Soc Mechanical Engineers2016