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Due to the electricity systems' increasing need for flexibility, demand side flexibility aggregation becomes more important. An issue is how to make such activities profitable, which may be obtained by selling flexibility in multiple markets. A challenge is to allocate volumes to the different markets in an optimal way, which motivates the need for advanced decision support models. In this paper, we propose a methodology for optimal bidding for a flexibility aggregator participating in three sequential markets. We demonstrate the approach in a generalized market design that includes an options market for flexibility reservation, a spot market for day-ahead or shorter and a flexibility market for near real-time dispatch. Since the bidding decisions are made sequentially and the price information is gradually revealed, we formulate the decision models as multi-stage stochastic programs and generate scenarios for the possible realizations of prices. We illustrate the application of the models in a realistic case study in cooperation with four industrial companies and one aggregator. We quantify and discuss the value of flexibility and find that our proposed models are able to capture most of the potential value, except for some extreme cases. The value of aggregation is quantified to 3 %.

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The recent deployment of distributed battery units in prosumer premises offer new opportunities for providing aggregated flexibility services to both distribution system operators and balance responsible parties. The optimization problem presented in this paper is formulated with an objective of cost minimization which includes energy and battery degradation cost to provide flexibility services. A decomposed solution approach with the alternating direction method of multipliers (ADMM) is used instead of commonly adopted centralised optimization to reduce the computational burden and time, and then reduce scalability limitations. In this work we apply a modified version of ADMM that includes two new features with respect to the original algorithm: first, the primal variables are updated concurrently, which reduces significantly the computational cost when we have a large number of involved prosumers; second, it includes a regularization term named Proximal Jacobian (PJ) that ensures the stability of the solution. A case study is presented for optimal battery operation of 100 prosumer sites with real-life data. The proposed method finds a solution which is equivalent to the centralised optimization problem and is computed between 5 and 12 times faster. Thus, aggregators or large-scale energy communities can use this scalable algorithm to provide flexibility services.

This paper presents an optimization framework for a load aggregator participating in the wholesale power market and the regulation capacity market. The objective of the aggregator is to minimize the total energy costs of a portfolio of energy consumers. The market organization is based on the Nordic model. The optimization model includes a detailed representation of the physical system at each consumer. Flexibility may come from load reductions, substitutions between energy carriers and from use of energy storage. A case study is performed using actual data from a set of Norwegian electricity consumers to test the model and estimate the value of aggregation in the current market framework.

Abstract-This paper defines a day-ahead micro-market structure and illustrates its capability of increasing distributed energy resources' integration. This micro-market mimics in the distribution level the structure of the current European dayahead markets and their rules to introduce competition, and is based on the social welfare indicator. Micro-markets could overcome two major challenges of pool markets: they could consider the distribution network to ensure feasibility of the matched configurations and they could handle a high penetration of renewable energy without generation costs. A micro-market is controlled and supervised by the micro-market operator who executes the auction algorithm. This paper exposes a state-ofthe-art about micro-markets, proposes a structure and a set of rules, and shows micro-market's behaviour in a case study. The results show that with under-sized distribution networks the micro-market can effectively improve the social welfare with respect to other simpler approaches.

The present paper defines the equations that govern the optimization model of an electric water heater in order to manage automatically its available flexibility for the user's own benefit. A case study that focuses on the electric water heater flexibility potential is carried out: it is aimed to minimize the total expected electricity cost of a single household that only has an electric water heater as a flexibility source. The temperature inside the hot water tank is an unknown value since the temperature sensor is not sending any information outside. Moreover, real hot water consumption is uncertain and the model takes decisions based on forecasting models. Given the limited information available, the model is developed in a conservative way with the objective of maintaining both, the hot water temperature and the user's comfort. The results show that it is possible to reduce the electricity bill by managing optimally the available flexibility from the electric water heater.