This paper presents a general description of local flexibility markets as a market-based management mechanism for aggregators. The high penetration of distributed energy resources introduces new ...flexibility services like prosumer or community self-balancing, congestion management and time-of-use optimization. This work is focused on the flexibility framework to enable multiple participants to compete for selling or buying flexibility. In this framework, the aggregator acts as a local market operator and supervises flexibility transactions of the local energy community. Local market participation is voluntary. Potential flexibility stakeholders are the distribution system operator, the balance responsible party and end-users themselves. Flexibility is sold by means of loads, generators, storage units and electric vehicles. Finally, this paper presents needed interactions between all local market stakeholders, the corresponding inputs and outputs of local market operation algorithms from participants and a case study to highlight the application of the local flexibility market in three scenarios. The local market framework could postpone grid upgrades, reduce energy costs and increase distribution grids’ hosting capacity.
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%.
•We propose bidding models for a flexibility aggregator.•The flexibility aggregator participates in three, sequential markets.•We use stochastic programming to handle that market prices are uncertain.•We perform a realistic case-study.•We quantify and discuss the value of flexibility and the value of aggregation.
•We propose a new optimization problem for scheduling flexible resources to meet distribution system operator requests.•We included loads, generators and batteries as flexibility resources.•The ...optimization problem minimizes the SESP operation cost.•We perform a case study to validate the work presented.•We perform a test in the laboratory platform.
The increasing penetration of distributed energy resources in the distribution grid is producing an ever-heightening interest in the use of the flexibility on offer by said distributed resources as an enhancement for the distribution grid operator. This paper proposes an optimization problem which enables satisfaction of distribution system operator requests on flexibility. This is a decision-making problem for a new aggregator type called Smart Energy Service Provider (SESP) to schedule flexible energy resources. This aggregator operates a local electricity market with high penetration of distributed energy resources. The optimization operation problem of SESP is formulated as an MILP problem and its performance has been tested by means of the simulation of test cases in a local market. The novel problem has also been validated in a microgrid laboratory with emulated loads and generation units. The performed tests produced positive results and proved the effectiveness of the proposed solution.
We propose short-term decision-support models for aggregators that sell electricity to prosumers and buy back surplus electricity. The key element is that the aggregator can control flexible energy ...units at the prosumers. Our objective is total cost minimization by trading in an electricity spot market also taking into consideration costs from grid tariffs, use of fuels and imbalance penalization. We explicitly model the flexibility properties of the underlying energy systems in the prosumers' buildings. In addition, we include the bidding rules and handle the interrelations between hours. Finally, we capture the information structure of uncertain parameters through scenario trees. This results in a two-stage stochastic mixed integer linear program where the bidding decision is made in the first stage and the scheduling in the second. We illustrate the approach in a case study with a diverse portfolio of prosumers. By simulating over a two-month period, we calculate the value of flexibility and the value of stochastic planning.
•We propose a new decision-support model for demand-side bidding and scheduling.•We explicitly represent the bidding process and handle interrelation between hours.•We take into account the prosumers' energy units and their flexibility properties.•We represent uncertain parameters in scenario trees.•We perform a case study to illustrate the properties of the model.
The decarbonization of the power sector involves electrification and a massive deployment of variable renewable energy sources, leading to an increase of local transmission congestion and ramping ...challenges. A possible solution to secure grid stability is local flexibility markets, in which prosumers can offer demand-side flexibility to the distribution system operator or other flexibility buyers through an aggregator. The purpose of this study was to develop a framework for estimating and offering short-term demand-side flexibility to a flexibility marketplace, with the main focus being baseline estimation and bid generation. The baseline is estimated based on forecasts that have been corrected for effects from earlier flexibility activations and potential planned use of internal flexibility. Available flexibility volumes are then estimated based on the baseline, physical properties of the flexibility asset and agreed constraints for baseline deviation. The estimated available flexibility is further formatted into a bid that may be offered to a flexibility marketplace, where buyers can buy and activate the offered flexibility, in whole or by parts. To illustrate and verify the proposed methodology, it was applied to a grocery warehouse. Based on real flexibility constraints, historic meter values, and forecasts for this use-case, we simulated a process where the flexibility is offered to a hypothetic flexibility marketplace through an aggregator.
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.
Due to technological developments and political goals, the electricity system is undergoing significant changes, and a more active demand side is needed. In this paper, we propose a new model to ...support the scheduling process for energy flexibility in buildings. We have selected an integrated energy carrier approach based on the energy hub concept, which captures multiple energy carriers, converters and storages to increase the flexibility potential. Furthermore, we propose a general classification of load units according to their flexibility properties. Finally, we define price structures that include both time-varying prices and peak power fees. We demonstrate the properties of the model in a case study based on a Norwegian university college building. The study shows that the model is able to reduce costs by reducing peak loads and utilizing price differences between periods and energy carriers. We illustrate and discuss the properties of two different approaches to deal with uncertain parameters: Rolling horizon deterministic planning and rolling horizon stochastic planning, the latter includes explicit modeling of the uncertain parameters. Although in our limited case, the stochastic model does not outperform the deterministic model, our findings indicate that several factors influence this conclusion. We recommend an in-depth analysis in each specific case.
•We propose a new model for the scheduling of energy flexibility in buildings.•We cover multiple energy carriers and include converter, storage and load units.•We classify load units according to their flexibility properties.•Our price structure covers different price regimes including peak fees.•We perform a case study and discuss two approaches to handle uncertain parameters.
This paper proposes principles for how to distribute the profits generated in flexibility market between the stakeholders in the explicit flexibility value chain. The principles are rooted in ...contract design theory. Revenue-sharing and profit-sharing contracts are discussed, in addition to one-part and two-part linear contracts. The model for the value chain is proposed with four categories of stakeholders, in addition to the flexibility marketplaces: the aggregator, the aggregator platform provider, the energy management system provider and the households. Motivation and added cost from participating in the explicit flexibility business based on households are discussed, and different contract models are proposed. Finally, a simplified, yet realistic, example is provided assuming a portfolio with 1 MW flexibility sold at the flexibility market and at the balancing market as manual frequency restoration reserve, yielding a yearly gross profit of 0.15 million euro, based on the Norwegian markets. The example shows how the revenues can be split between all stakeholders and provides a good reference point for further improvements of the profit-sharing principles.
This paper exposes a flexibility management algorithm to optimize the operation of behind-the-meter charging infrastructure in a building including external flexibility requests from the local ...distribution system operator. It includes the electricity cost minimization including drivers comfort cost and it uses the limited information available in conventional slow charging points like electricity consumption and charging point status.
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