Aggregation of Households in Community Energy Systems: An Analysis from Actors’ and Market Perspectives

Abstract

In decentralized energy systems, electricity generated and flexibility offered by households can be organized in the form of community energy systems. Business models, which enable this aggregation at the community level, will impact on the involved actors and the electricity market. For the case of Germany, in this paper different aggregation scenarios are analyzed from the perspective of actors and the market. The main components in these scenarios are the Community Energy Storage (CES) technology, the electricity tariff structure, and the aggregation goal. For this evaluation, a bottom-up community energy system model is presented, in which the households and retailer are the key actors. In our model, we distinguish between the households with inflexible electricity load and the flexible households that own a heat pump or Photovoltaic (PV) storage systems. By using a game-theoretic approach and modeling the interaction between the retailer and households as a Stackelberg game, a community real-time pricing structure is derived. To find the solution of the modeled Stackelberg game, a genetic algorithm is implemented. To analyze the impact of the aggregation scenarios on the electricity market, a “Market Alignment Indicator” is proposed. The results show that under the considered regulatory framework, the deployment of a CES can increase the retailer’s operational profits while improving the alignment of the community energy system with the signals from the electricity market. Depending on the aggregation goal of the retailer, the implementation of community real-time pricing could lead to a similar impact. Moreover, such a tariff structure can lead to financial benefits for flexible households.

A Cooperative Game-Based Sizing and Configuration of Community-Shared Energy Storage

“…where β is the annualizing operator for the calculation of annual CSES investment and maintenance cost, which is defined in Equation (3); α is annual interest rate; Year is the life span of energy storage batteries; c bat is the unit capacity investment cost of energy storage; c o&m is the unit capacity maintenance cost of energy storage; E W (•) means expectations value in multiple scenarios; C i ope (k,w) is the daily operation cost of user i under scenario w in year k, as shown in Equation ( 5); λ t b and λ t s are the purchasing and selling price of electricity, respectively, at time t; P i,t b (k,w) and P i,t s (k,w) are the purchasing and selling electricity of user i, respectively, at time t under scenario w in year k; C loss (k,w) is the daily power loss cost of energy storage under scenario w in year k, which is defined in Equation (6); c e (k) is the power loss cost factor in year k, which gradually increases with the operation time of energy storage; P t c (k,w) and P t d (k,w) are the charge and discharge power of energy storage, respectively, at time t under scenario w in year k; N is the total number of community users.…”

“…The global energy consumption structure is transiting rapidly [4]. In recent years, with the increasing user-side electricity demand and the installed capacity of intermittent distributed energy resources [5], user-side energy storage is playing an increasingly important role in the grid [6]. However, the high investment cost of user-side energy storage and the low feed-in tariff severely limit the development of user-side energy storage [7,8].…”

Integrating Behavioural Aspects in Energy System Modelling—A Review

“…Ropuszy ńska-Surma and Węglarz [124] find that the area of residence, building type, age and income are significant determinants of the households' willingness to install renewables in Poland. They also mention that the adoption of low-carbon electricity generation technologies is closely linked to the discussion on so-called prosumers (consumers that also become producers) [125], which in turn is closely linked to aspects of energy autonomy or autarky [126] as well as self-sufficiency and self-consumption [127][128][129].…”

Effects of heat pump scheduling on low‐voltage grids using a receding horizon control strategy

“…In ref. [43], the impact of household (HH) aggregation from a user and market perspective is investigated by mapping the interaction between HHs and a retailer through a game theoretic approach. The results of the study show that aggregation is economically beneficial for both parties and leads to a better match of generation and demand.…”