Selfish Energy Sharing in Prosumer Communities: A Demand-Side Management Concept

Abstract

Global warming is endangering the earth's ecosystem. It is imperative for us to limit green house gas emissions in order to combat rising global average temperatures. One way to move forward is the integration of renewable energy resources on all levels of the power system, i.e. from large-scale energy producers to individual households. The future smart grid provides the technology for this. In this paper, a novel demand-side management concept is proposed. It is implemented by a utility company which focuses on renewable energy. Through a specific billing mechanism, prosumers are encouraged to balance load and supply. A gametheoretic approach models households as self-determined rational energy users, that want to reduce their individual electricity costs. To achieve this, they selfishly share energy with their neighbours and also schedule their energy storage systems. The scheme is designed such that monetary transactions between households are not necessary. Thus, it provides an alternative approach to energy trading schemes from the literature.

Game theory to enhance stock management of Personal Protective Equipment (PPE) during the COVID-19 outbreak

“…This can be enhanced by adding stochastic elements that capture the uncertainty in demand [30]. The model can also be extended by investigating scenarios where hospitals can share their resources by having mutual agreements or using the selfish sharing approach proposed in [56]. This could be via using a scheme to incentivise matching scheduled regional demand with the production of the supplier.…”

Reducing Grid Distortions Utilizing Energy Demand Prediction and Local Storages

“…Global average temperatures are rising due to the increasing amount of greenhouse gas emissions causing natural disasters and having negative impact to nature and humans [1]. As currently the largest share of CO 2 emissions are caused by burning of fossil fuels, renewable energy sources are increasingly employed in order to reduce the carbon footprint of the primary energy sector [2].…”

“…wind or sunshine [3]. Therefore, to account for the increasing volatility of energy generation, combined with the volatility of energy demand, three major topics have been addressed in literature in order to reduce grid distortions and peak loads, namely load prediction, integration of storages and demand management [1], [4]. Especially, when considering storage optimizations or advanced demand management usually a load prediction architecture is combined with a shared storage unit in order to find optimal charging/discharging strategies under certain constraints.…”

“…The physical models are based on thermodynamic coupling for precise energy analysis and use parameters such as building construction details, operation schedules, climate characteristics, etc. [1], [5]. Due to the high number of parameters and the complex interactions, an accurate estimation of the energy consumption is intrinsically difficult.…”

“…Conversely, data driven models use recorded data and try to predict the demand based on past energy consumption patterns. The data driven methods have the advantage of faster calculation, while being suitable for non-linear modelling [1]. Since the prediction of energy consumption is a difficult task due to the irregular components appearing in the energy consumption signal [10], several different methods for improving the load prediction accuracy have been proposed.…”