Design, Valuation and Comparison of Demand Response Strategies for Congestion Management

Abstract

Decarbonisation of heat and transport will cause congestion issues in distribution networks. To avoid expensive network investments, demand flexibility is necessary to move loads from peak to off-peak periods. We provide a method and metric for assessing and selecting the optimal demand response strategy for a given network congestion scenario and applied it to a case study network in Coleraine, Northern Ireland. We proposed a Price Approximation/Mean Grouping strategy to deal with the issue of congestions occurring at the lowest-price period in real-time pricing schemes. The Mean Grouping strategy increased the average lowest-price hours from 1.32 to 3.76. We show that a three-cluster tariff is effective in solving medium congestion issues in Northern Ireland and could save consumers an average of £117/year on their heating bill. However, for networks with low headroom suffering from serious congestion issues, a smart control strategy is needed.

Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study

“…The loads classification based on their intrinsic nature [21] can be useful only for preliminary analysis (see Figure 1), providing to designers with a flexibility amount overestimation: (i) all those loads deemed as flexible in accordance with the classification might be not-flexible related to the specific management strategy [22,23]; (ii) other loads might be not-flexible caused by different technical constraints related to the household appliances [24]. Those ones, can be eventually imposed by a building energy management system (e.g., BEMS) [25].…”

Power Grid Integration and Use-Case Study of Acid-Base Flow Battery Technology

“…In low voltage network congestion use-cases [49,55,56], multiple ABFB systems would be installed at weak points of the distribution network, such as along radial feeder lines, to add flexibility to the grid, allowing for increased integration of renewables without jeopardizing grid stability. Locating the ESS at distribution level enables network congestion relief as well as allowing for the deferral of upgrades to the distribution network infrastructure.…”

Techno-economic analysis of energy storage within network constraint groups for increasing the share of variable renewable energy

“…In [42], through an 11 kV substation of a campus distribution network, a 2 MW/4 MWh storage was deployed to increase self-consumption of wind energy; new wind turbines and batteries were simulated for flexibility gains within the distribution network in [49]. Demand response strategies have been described to manage network congestions while providing ancillary services through the network using different levels of load portfolios in [50], moving loads from peak to off-peak periods in [51]; and residential batteries are described for peak shaving in [52].…”