An improved decomposition based multi-objective evolutionary algorithm for the operation management of a renewable micro-grid

This paper explores the economic viability of renewable energy microgrids in remote regions, focusing on their potential to provide reliable and sustainable energy solutions. Renewable energy microgrids, which integrate sources such as solar and wind power with advanced energy storage, offer a promising alternative to traditional energy systems, particularly for regions that lack access to conventional power grids. Remote regions often face significant challenges in accessing reliable and affordable energy. The dependence on diesel generators leads to high operational costs and environmental concerns. Renewable energy microgrids present an innovative solution to these issues by harnessing locally available renewable resources, reducing greenhouse gas emissions, and providing consistent energy supply. The assessment involves analyzing initial capital investment and operational costs, comparing the economic performance of renewable microgrids against conventional energy systems, evaluating environmental and social benefits, and reviewing government policies, incentives, and regulatory frameworks that impact the deployment of microgrids. Data is gathered from case studies, existing projects, academic research, and industry reports to develop economic models and scenarios for analysis. While the initial setup cost for renewable microgrids is relatively high, advancements in technology and increased production scale are driving costs down. Significant savings are achieved over time through reduced fuel and maintenance expenses compared to diesel generators. Microgrids can achieve a favorable return on investment (ROI) within 5-10 years, contingent upon local conditions and system scale. Adoption of renewable microgrids leads to substantial reductions in greenhouse gas emissions, supporting global climate goals. Improved energy reliability enhances the quality of life, supports economic development, and promotes social welfare in remote communities. Effective policies, subsidies, and incentives are crucial for reducing financial barriers and encouraging investment in renewable microgrids. Establishing clear and supportive regulatory environments is essential for the successful integration and scalability of microgrid systems. Despite the promising outlook, several challenges remain. The significant upfront investment required for microgrid infrastructure is a major hurdle. The lack of technical skills for installation, operation, and maintenance in remote regions can hinder successful deployment. Additionally, variability in local renewable resources like solar and wind can affect the reliability and performance of microgrids. To overcome these challenges, it is recommended to focus on reducing costs through technological advancements and enhancing local capacity through training and education. Developing policies and incentives that support renewable energy projects and reduce financial barriers is also essential. Engaging local communities in the planning and implementation process ensures the systems meet their needs and gain local support. Renewable energy microgrids are economically viable and environmentally sustainable solutions for providing reliable energy in remote regions. Despite initial cost challenges, the long-term benefits, including operational savings, emission reductions, and enhanced energy access, make them a compelling option. With strategic investments, supportive policies, and community involvement, renewable microgrids can significantly transform energy access and drive sustainable development in remote areas. Keywords: Economic Viability, Microgrinds, Sustainability, Energy, Nigeria.

Optimal scheduling of the battery energy storage (BES) in a microgrid is presented in this paper. The microgrid model in this paper consists of a photovoltaic (PV) power plant, a wind generator (WG), battery energy storage (BES), load, and diesel generator (DG). BES is required to store the generation excess from renewable energy sources (RES). BES also can store energy from the main grid but optimization is required to decide when to charge and discharge the battery. The optimization goal is to minimize electricity costs when the main grid is available and fuel costs when it is not available. The optimization problem in this paper is a linear programming (LP) problem that is modeled and solved with Python Gurobi. Optimization results are presented for different cases. A DG is used only when the main grid is not available due to the high fuel price. Electricity cost from DG is 5.28 times higher than the cost from the grid for the first case and 4.36 times higher for the second case. There is no generation excess in the second case so the battery is not used when the main grid is not available.

Network loss minimization with voltage security using differential evolution

11 - Risk-averse scheduling of virtual power plants considering electric vehicles and demand response

<span>This paper proposes a new optimal operation of Microgrids (MGs) in a distribution system with wind energy generators (WEGs), solar photovoltaic (PV) energy systems, battery energy storage (BES) systems, electric vehicles (EVs) and demand response (DR). To reduce the fluctuations of wind, solar PV powers and load demands, the BES systems and DR are utilized in the proposed hybrid system. The detailed modeling of WEGs, solar PV units, load demands, BES systems and EVs has been presented in this paper. The objective considered here is the minimization of total operating cost of microgrid, and it is formulated by considering the cost of power exchange between the main power grid and microgrid, cost of wind and solar PV energy systems, cost of BES systems, EVs and the cost due to the DR in the system. Simulations are performed on a test microgrid, and they are implemented using GAMS software. Various case studies are performed with and without considering the proposed hybrid system.</span>

The off-grid village electrification is made viable by utilizing the full potential offered by renewable energy sources (RESs). Integrating more than one resource i.e. wind and solar, offers self-sufficiency of power due to their complementary nature. The intermittency issues raised by RESs are overcome by using a battery as the storage. The battery reduces the uncertainty during demand rise, power deficiency and overloading with its faster response than diesel generators and micro-turbines. This work deals with the wind energy/solar photovoltaic (PV)/battery energy storage (BES) operating in coordinated manner to meet the high power residential loads at village scale. For the required speed regulation of a wind turbine driven generator, the traditional proportional integral (PI) controller is replaced by evolution inspired biogeography based optimization control (BBO) based PI controller. The relevant issues identified by a use of fixed gain PI controller such as, unsuitable regulation due to incorrect tuning are addressed by the use of BBO tuned speed controller providing fine-tuning of the weighting factors. Whereas, in the voltage control, the performance degradation issue of PI controller while dealing with AC quantities, is dealt with adaptive proportional resonant (PR) controller along with disturbance observer based phase locked loop (DO-PLL). The DO control filters the load current component and improves the power quality at point of common coupling (PCC). The battery is deployed through a bidirectional converter, acts as a residential storage for backing up the power firming RESs production. Performance improvement obtained from a laboratory developed test setup verifies the microgrid effectiveness at RESs generation variability and load changes.

The peer-to-peer energy sharing (PPES) program is considered an effective measure to improve the resilience of microgrids. This study quantifies this impact from a long-period perspective by calculating and comparing buildings' probability of surviving outage (PSO) with given length of outage sustained hour in renewable microgrid under different PPES scenarios. The results indicate that the PSO can effectively represent the influence of PPES on enhancing microgrid resilience across various load levels. Specifically, buildings with load patterns more consistent with solar power output pattern have a higher probability to survive longer power outages. Meanwhile, PPES is more effective than load curtailment in improving a building's resilience, this is because only if the load is curtailed by almost 50% can the resilience be improved to the level when shared energy was used. Simultaneously, load curtailment reduces the Renewable Energy Utilization Ratio (REUR) of buildings, whereas introducing PPES maximizes the REUR. Finally, the combination of 20% load curtailment + PPES can almost generate the same resilience improving effect for the microgrid as that of each building alone installing photovoltaic + battery energy storage, and if higher amount of load can be curtailed, a greater resilience improvement can be achieved. Therefore, for the sake of adaptability as well as resource conservation and sustainability, we recommended policymakers and other stakeholders put a higher priority on the implementation of PPES + moderate amount of load curtailment in improving the resilience of microgrids.

Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero.

The distribution system operator (DSO) needs an optimal day-ahead scheduling (ODAS) for the economic and sustainable supply of electrical energy considering input parameters such as the price of the upstream grid. Next-generation distribution networks or smart distribution networks are the future networks where responsive loads are available. Renewable sources include wind turbines and solar panels have expanded. The presence of electric vehicles and the purchase from the electricity upstream market is provided, and the network can be programmed and controlled through the devices which record and transmit information. In this chapter, a robust optimization (RO) method has been proposed to minimize the cost of ODAS of smart distribution system (SDS) considering load-responsive and renewable energy sources (RESs) such as wind turbine (WT) and nonrenewable sources such as diesel generators (DGs) and battery energy storage system (BESS). The proposed method considers all the technical constraints of the upstream grid and DGs and the utilized BESS. In order to model SDSs, a 33-base IEEE test system has been used in the evaluation of the proposed model. The proposed ODAS concept determines the optimal level of exchange with the upstream network, the production of each dispersed generation unit, and the participation of demand response (DR) programs. It also provides an optimal layout for charging and discharging the BESS. It can be observed that the proposed model has the optimal scheduling capabilities of the SDSs considering the uncertainties of power market price. Moreover, it is observed that the resilient optimization method reduces the cost of network operation and confronts the price of electricity with uncertainty.

Microgrids can be used in demand response (DR) and islanding operations. This paper explores a real-world microgrid implementation performed at the University of California, Riverside. The intended purpose is to see the feasibility of demand response and islanding in a real microgrid. An algorithm is used to generate “target data” for the Battery Energy Storage (BES) Inverter to follow. The other purpose is to see the accuracy when transitioning from a simulated model to a real-life implementation. The results from this implementation show it is feasible to respond to demand response scenarios with the current setup. However, more research is needed to successfully implement islanding responses due to infrequent power spikes that briefly draw more power than the microgrid can produce.

With the widespread installation of smart meters, more and more residents are signing long-term contracts with the grid for demand response (DR) scheme. As an effective way of managing energy consumption about energy prices and system energy balance conditions, the DR scheme contributes to the system cost-saving. In this context, the optimal operation of a multi-energy residential microgrid (MERMG) is studied considering the multi-energy DR scheme. The objective is to minimize the net operating cost under the system’s operational constraints. In the multi-energy DR scheme, the price-based DR (PBDR) is used for power consumption and thermal (heat or cooling) inertia is for the thermal energy consumption. Besides, to guarantee the solution quality as well as relieve the computation burden, the original nonlinear items are linearized to make the operation model a mixed-integer linear programming (MILP) problem. Then it can be then efficiently solved by commercial solvers like Cplex and Gurobi. Finally, by applying the proposed method in the test MERMG and comparing it with the one without the multi-energy DR scheme, the validity of the proposed method is verified.

Do the Dam Project—Evaluating floating solar photovoltaic and energy storage at Inanda Dam within eThekwini Municipality, South Africa

Recently, the development of microgrid system is growing rapidly. Energy such as sun, wind, ocean, wave currents, and geothermal are alternative energy in developing Renewable Energy Sources (RES) in the microgrid system. Most commonly hybrid RES system in the microgrid is a combination between Photovoltaic (PV), ESS and diesel generation. To implement the hybrid RES, the combination strategy control is needed. One of the main motivations using energy management in the microgrid is that they are capable of managing and coordinating diesel generators, storages, RES and loads to optimize the RES for cost efficiency. In this paper, the optimized energy management for the hybrid RES system combined by PV, Li-ion battery ESS, and diesel generator is proposed. To accommodate the diesel generation condition, the developed control strategy is the economic dispatch for fuel cost optimization in diesel generation using combination of the Lagrange multiplier and lambda iteration method and the optimization in energy management operation using an Adaptive Model Predictive Control (AMPC) with the lifetime constraint of batteries. Finally, the proposed control algorithm is verified through the PSiM simulation results.

Optimal coordination strategy of demand response and electric vehicle aggregators for the energy management of reconfigured grid-connected microgrid

Integration of smart buildings with high penetration of storage systems in isolated 100 % renewable microgrids