Reinforcement learning in local energy markets

Local energy markets (LEMs) are well suited to address the challenges of the European energy transition movement. They incite investments in renewable energy sources (RES), can improve the integration of RES into the energy system, and empower local communities. However, as electricity is a low involvement good, residential households have neither the expertise nor do they want to put in the time and effort to trade themselves on their own on short-term LEMs. Thus, machine learning algorithms are proposed to take over the bidding for households under realistic market information. We simulate a LEM on a 15 min merit-order market mechanism and deploy reinforcement learning as strategic learning for the agents. In a multi-agent simulation of 100 households including PV, micro-cogeneration, and demand shifting appliances, we show how participants in a LEM can achieve a self-sufficiency of up to 30% with trading and 41,4% with trading and demand response (DR) through an installation of only 5kWp PV panels in 45% of the households under affordable energy prices. A sensitivity analysis shows how the results differ according to the share of renewable generation and degree of demand flexibility

Increasing the efficiency of local energy markets through residential demand response

Local energy markets (LEMs) aim at building up local balances of generation and demand close to real time. A bottom-up energy system made up of several LEMs could reduce energy transmission, renewable curtailment and redispatch measures in the long-term, if managed properly. However, relying on limited local resources, LEMs require flexibility to achieve a high level of self-sufficiency. We introduce demand response (DR) into LEMs as a means of flexibility in residential demand that can be used to increase local self-sufficiency, decrease residual demand power peaks, facilitate local energy balances and reduce the cost of energy supply. We present a simulation study on a 100 household LEM and show how local sufficiency can be increased up to 16% with local trading and DR. We study three German regulatory scenarios and derive that the electricity price and the annual residual peak demand can be reduced by up to 10ce/kWh and 40

South African power market : a full-cost analysis of future technology options for electricity generation

The aim of this research is to facilitate informed decision making for an optimal allocation of future electricity generation resources in South Africa. Such a study is important in order to find out which technologies are economically favorable from a long-term perspective for a growing and emission-intensive emerging economy that bases its energy production predominantly on the depletion of its domestic fossil coal resources. The research approach adopted in this dissertation is based on a profound review of literature on South Africa’s electricity market, coupled with the development of a full-cost approach based on levelized cost of electricity. This approach is used to empirically evaluate the performance of new-build technologies including coal, nuclear, natural gas, solar PV, CSP and wind with regard to economic, environmental and social criteria. The findings from this research provide evidence that coal power stations are not the optimal option for electricity generation in South Africa and that wind, CSP, PV and nuclear are to be preferred for new investments. The main conclusion drawn from this study is that the inclusion of indirect costs and non-monetary aspects of electricity generation makes technologies competitive in South Africa that seem expensive from a pure-economic point of view. The dissertation recommends that the structure of the South African electricity market should be improved to facilitate the accommodation of higher shares of renewable energy

Increasing the efficiency of local energy markets through residential demand response

Abstract Local energy markets (LEMs) aim at building up local balances of generation and demand close to real time. A bottom-up energy system made up of several LEMs could reduce energy transmission, renewable curtailment and redispatch measures in the long-term, if managed properly. However, relying on limited local resources, LEMs require flexibility to achieve a high level of self-sufficiency. We introduce demand response (DR) into LEMs as a means of flexibility in residential demand that can be used to increase local self-sufficiency, decrease residual demand power peaks, facilitate local energy balances and reduce the cost of energy supply. We present a simulation study on a 100 household LEM and show how local sufficiency can be increased up to 16% with local trading and DR. We study three German regulatory scenarios and derive that the electricity price and the annual residual peak demand can be reduced by up to 10c€/kWh and 40%