Local Balancing of the Electricity Grid in a Renewable Municipality; Analyzing the Effectiveness and Cost of Decentralized Load Balancing Looking at Multiple Combinations of Technologies

Abstract

With the integration of Intermitted Renewables Energy (I-RE) electricity production, capacity is shifting from central to decentral. So, the question is if it is also necessary to adjust the current load balancing system from a central to more decentral system. Therefore, an assessment is made on the overall effectiveness and costs of decentralized load balancing, using Flexible Renewable Energy (F-RE) in the shape of biogas, Demand Side Management (DSM), Power Curtailment (PC), and electricity Storage (ST) compared to increased grid capacity (GC). As a case, an average municipality in The Netherlands is supplied by 100% I-RE (wind and solar energy), which is dynamically modeled in the PowerPlan model using multiple scenarios including several combinations of balancing technologies. Results are expressed in yearly production mix, self-consumption, grid strain, Net Load Demand Signal, and added cost. Results indicate that in an optimized scenario, self-consumption of the municipality reaches a level of around 95%, the total hours per year production matches demand to over 90%, and overproduction can be curtailed without substantial losses lowering grid strain. In addition, the combination of balancing technologies also lowers the peak load to 60% of the current peak load in the municipality, thereby freeing up capacity for increased demand (e.g., electric heat pumps, electric cars) or additional I-RE production. The correct combination of F-RE and lowering I-RE production to 60%, ST, and PC are shown to be crucial. However, the direct use of DSM has proven ineffective without a larger flexible demand present in the municipality. In addition, the optimized scenario will require a substantial investment in installations and will increase the energy cost with 75% in the municipality (e.g., from 0.20€ to 0.35€ per kWh) compared to 50% (0.30€ per kWh) for GC. Within this context, solutions are also required on other levels of scale (e.g., on middle or high voltage side or meso and macro level) to ensure security of supply and/or to reduce overall costs.

Trends in Locally Balanced Energy Systems without the Use of Fossil Fuels: A Review

“…The authors in [108] proposed locally balancing the power grid in a commune with a high degree of RES penetration. To achieve high self-sufficiency, load balancing should be decentralized at the level of over 90%, while improving the integration of RES.…”

“…As challenges and obstacles, the authors of the publication propose the development of solutions at other scale levels to ensure the security of supply. The development of energy storage systems will also be a milestone [108].…”

Optimal Controllers and Configurations of 100% PV and Energy Storage Systems for a Microgrid: The Case Study of a Small Town in Jordan

“…The electrical demand trend in global consumerism requires improvements in power generation methods. In addition, there is a need to meet the growth in demand and develop a more cost-efficient energy system by motivating the consideration of new generation methods, in particular, distribution generation, renewable energy, and energy storage technologies [4]. The concerns of energy costs, climate change, and fossil fuel depletion have led many countries to move towards low-polluting Renewable Energy Sources (RES) such as Photovoltaic (PV), which offer cost-effective and environmental friendliness options [4,5].…”

“…In addition, there is a need to meet the growth in demand and develop a more cost-efficient energy system by motivating the consideration of new generation methods, in particular, distribution generation, renewable energy, and energy storage technologies [4]. The concerns of energy costs, climate change, and fossil fuel depletion have led many countries to move towards low-polluting Renewable Energy Sources (RES) such as Photovoltaic (PV), which offer cost-effective and environmental friendliness options [4,5]. algorithm called the Golden Ratio Optimization Method (GROM) with the motivation of achieving the global solution within an efficient time for complex problems.…”