Flexibility coming from consumers in residential and service sectors has received significant attention to deal with uncertainty and variability of renewable energy sources. Since these consumers are too small individually to participate in the electricity markets, their assets can be pooled by an aggregator. The aggregator can implement business models by trading flexibility obtained from these consumers’ assets in different electricity markets. However, the aggregator and the consumers are only motivated to implement a business model, if it is economically feasible. The economic feasibility of a business model depends on (1) financial aspects: how much profit the aggregator makes, and how much money the consumers save, and (2) operational aspects: how the consumers’ assets are operated to increase the financial aspects. This paper aims to provide insights in these operational and financial aspects of the aggregator's business models in residential and service sectors. For this purpose, a literature review is conducted, and a framework is presented to analyze the selected papers on these operational and financial aspects. Based on this analysis, different strategies for the aggregator to implement business models are determined. Moreover, knowledge gaps are identified and several recommendations for future research are provided.

Aggregators are considered essential to obtain flexibility from small residential and service sector consumers. They can implement business models by trading flexibility from their consumers' assets in various electricity markets. The aim of this paper is to identify challenges faced by aggregators with different roles, while implementing business models. We consider aggregators possessing three roles: of a supplier, Balance Responsible Party (BRP), and of an independent aggregator. The results show that challenges identified create higher complexity for aggregators with BRP's role and independent aggregators to implement business models, while it is significantly easier for aggregators with supplier's role. Recommendations are given to overcome the higher complexity: standardization of contracts and raising consumers awareness. These recommendations facilitate aggregators with different roles to implement their business models, and enable a healthy competition in electricity markets.

The high level of uncertainty of renewable energy sources generation creates differences between electricity supply and demand, endangering the reliable operation of the power system. Demand response has gained significant attention as a means to cope with uncertainty of renewable energy sources. Demand response of residential and service sector consumers, when accumulated and managed by aggregators, can play a role in existing electricity markets. This paper addresses the question to what extent aggregator-mediated demand response can be used to deal with the impacts of the uncertainty of solar generation. Uncertain solar generation leads to imbalances of an aggregator. These imbalances can be reduced by shifting flexible loads, which is called demand response for internal balancing. The aim of this paper is to assess the impact of demand response from loads in residential and service sectors for internal balancing to reduce the imbalances of an aggregator, caused by uncertain solar generation. For this purpose, a Model Predictive Control model which minimizes the imbalances of the aggregator through load shifting is presented. The model is applied to a realistic case study in the Netherlands. The results show that demand response for internal balancing succeeds in reducing imbalances. Even though this is favorable from the power system's perspective, economic analysis shows that the aggregator is not financially incentivized to implement demand response for internal balancing.

Consumer batteries, when bundled by an aggregator, can provide flexibility by being used for consumers self-consumption and by offering Frequency Containment Reserve (FCR). Combining these two can also generate additional revenues for consumers since the aggregator pays an allowance to the consumers for FCR, called FCR price. The aim of this paper is determine the optimal share of consumer batteries that should be reserved for self-consumption and FCR to minimize the consumer's cost, while considering various FCR prices and investment cost of batteries which is paid by either consumers or the aggregator. For this purpose, an optimization model is presented, and applied to a case study in the Netherlands. The results show the consumers annual cost is minimized by finding the optimal FCR share of batteries. Despite that, investing in the battery is still not profitable for the consumers, unless the aggregator invests in the batteries.

The increase in variable renewable energy sources requires more flexibility in the power system. This could be provided by small distributed energy sources located at end-users. Aggregators can potentially exploit this flexibility by providing Primary Control Reserve (PCR) services to the Transmission System Operator, and congestion management services to Distribution System Operators. A simulation model based on Linear Programming is used to assess, for a case study in the Netherlands, whether such congestion management services are efficient. The results show that an aggregator can reduce the total system costs with 10-15% relative to traditional grid reinforcements, depending on price and depreciation of batteries, PCR remuneration, and wholesale electricity price.

Aggregators are considered essential to extend demand response (DR) to small residential and service sector consumers. Both sectors currently have untapped load flexibility, which is considered key to support renewable resource integration. Aggregators can offer this flexibility in bulk to other power system parties. This paper addresses the question under which conditions DR can be profitable for both aggregators and end-consumers. The paper builds further on existing research that shows end-consumer preference for flat-rate tariffs. The aim is to find the range of flat-rate retail prices for different photovoltaic (PV) feed-in-Tariffs which make DR profitable for both aggregator and end-consumers. For this purpose, an optimisation model which minimises costs through load scheduling is presented. The model is applied using two approaches: optimising from aggregator's and from end-consumers' perspective. The results show that only the aggregator's perspective yields a range of flat-rate retail prices that are profitable for both actors. However, both the price range and the expected profits of DR are small.