Allocation of locally generated electricity in renewable energy communities

This paper introduces a methodology to perform an ex-post allocation of locally generated electricity among the members of a renewable energy community. Such an ex-post allocation takes place in a settlement phase where the financial exchanges of the community are based on the production and consumption profiles of each member. The proposed methodology consists of an optimisation framework which (i) minimises the sum of individual electricity costs of the community members, and (ii) can enforce minimum self-sufficiency rates --proportion of electricity consumption covered by local production-- on each member, enhancing the economic gains of some of them. The latter capability aims to ensure that members receive enough incentives to participate in the renewable energy community. This framework is designed so as to provide a practical approach that is ready to use by community managers, which is compliant with current legislation on renewable energy communities. It computes a set of optimal repartition keys, which represent the percentage of total local production given to each member -- one key per metering period per member. These keys are computed based on an initial set of keys provided in the simulation, which are typically contractual i.e., agreed upon between the member and the manager the renewable energy community. This methodology is tested in a broad range of scenarios, illustrating its ability to optimise the operational costs of a renewable energy community.Comment: 8 pages, 6 figures, 4 tables, submitted to IEEE Transactions on Power System

Policy transfer using Value Function as Prior Information

This work proposes an approach based on reward shaping techniques in a reinforcement learning setting to approximate the opti- mal decision-making process (also called the optimal policy) in a desired task with a limited amount of data. We extract prior information from an existing family of policies have been used as a heuristic to help the construction of the new one under this challenging condition. We use this approach to study the relationship between the similarity of two tasks and the minimal amount of data needed to compute a near-optimal pol- icy for the second one using the prior information of the existing policy. Preliminary results show that for the least similar existing task consid- ered compared to the desired one, only 10% of the dataset was needed to compute the corresponding near-optimal policy

Empirical Analysis of Policy Gradient Algorithms where Starting States are Sampled accordingly to Most Frequently Visited States

In this paper, we propose an extension to the policy gradient algorithms by allowing starting states to be sampled from a probability distribution that may differ from the one used to specify the reinforcement learning task. In particular, we suggest that, between policy updates, starting states should be sampled from a probability density function which approximates the state visitation frequency of the current policy. Results generated from various environments clearly demonstrate a performance improvement in terms of mean cumulative rewards and substantial update stability compared to vanilla policy gradient algorithms where the starting state distributions are either as specified by the environment or uniform distributions over the state space. A sensitivity analysis over a subset of the hyper-parameters of our algorithm also suggests that they should be adapted after each policy update to maximise the improvements of the policies

Ex-post allocation of electricity and real-time control strategy for renewable energy communities

A presentation that describes recent algorithmic developments for operating a renewable energy community to minimize energy costs

Allocation of locally generated electricity in renewable energy communities

Local electricity markets represent a way of supplementing traditional retailing contracts for end consumers—among these markets, the renewable energy community has gained momentum over the last few years. This paper proposes a practical and readily to be adopted modelling solution for these communities, one that allows their members to share the economic benefits derived from them. The proposed solution relies on an ex-post allocation of the electricity that is generated within energy communities (i.e., local electricity) based on the optimisation of repartition keys. Repartition keys are therefore optimally computed to represent the proportion of total local electricity to be allocated to each community member, and aim to minimise the sum of electricity bills of all community members. Since the optimisation takes place ex-post the repartition keys do not modify the actual electricity flows, but rather the financial flows of the community members. Then, the billing process of the community will take these keys into account to correctly send the electricity bills to each member. Building on this concept, we also introduce two additions to the basic algorithm to enhance the stability of the community, which a global bill minimisation may fail to ensure (e.g., very asymmetrical solutions between members may lead to some of them opting out). The first addition is the computation of the self-sufficiency rates of the community members, defined as the proportion of the electricity demand covered by local electricity—this can be used to ensure a more even allocation of the local electricity among the community members, effectively acting as a revenue sharing system. The second addition is the use of initial repartition keys based on which the optimised ones are computed, and a tolerance on the maximum deviations between both sets of keys—this can help decrease the uncertainty of potential community members prior to their participation, as it ensures a minimum— ontractual—level of revenue for each of them. We have tested our initial methodology with a broad range of scenarios illustrating its ability reduce the electricity bills of the community members. Likewise, the two additions are tested showcasing the impact of revenue sharing by means of self-sufficiency rates and initial keys. Our results show that creating a community using this methodology can potentially reduce the electricity costs for all community members, and that self-sufficiency rates and initial keys can be used to stabilise the community by performing revenue sharing among them

Financial Optimization of Renewable Energy Communities Through Optimal Allocation of Locally Generated Electricity

peer reviewedThis paper proposes a practical modelling solution to the problem of sharing distributed renewable electricity generation in the context of renewable energy communities. According to this approach, the economic benefits of community members, derived from their participation in the community, are shared by means of repartition keys, which represent the proportion of total local electricity to be allocated, ex-post, to each community member. These keys are computed through a centralised optimisation framework that optimally allocates the electricity generated within the community (i.e., local electricity) among the community members so as to minimise the sum of the electricity bills of all community members. The electricity bill sent to each community member can be directly extracted from this solution. Building on this concept, we also introduce two additions to the basic algorithm, aiming to enhance the stability of the community, which a global bill minimisation may fail to ensure (e.g., very asymmetrical solutions between members may lead to some of them opting out). The first addition is the computation of the self-sufficiency rates of the community members, defined as the proportion of the electricity demand covered by local electricity, which can be exploited as a revenue sharing mechanism. The second addition is the use of initial repartition keys based on which the optimised ones are computed, so as to ensure a minimum contractual level of revenue for each of them. We have tested our methodology with a broad range of scenarios illustrating its ability to reduce the sum of the electricity bills of the community members and to share the revenues ensuring the stability of the renewable energy community. Our results show that creating a community using this methodology can potentially reduce the electricity costs for all community members, and that self-sufficiency rates and initial keys can be used to stabilise the community by performing revenue sharing among them. © 2013 IEEE

Financial Optimization of Renewable Energy Communities Through Optimal Allocation of Locally Generated Electricity

This paper proposes a practical modelling solution to the problem of sharing distributed renewable electricity generation in the context of renewable energy communities. According to this approach, the economic benefits of community members, derived from their participation in the community, are shared by means of repartition keys, which represent the proportion of total local electricity to be allocated, ex-post, to each community member. These keys are computed through a centralised optimisation framework that optimally allocates the electricity generated within the community (i.e., local electricity) among the community members so as to minimise the sum of the electricity bills of all community members. The electricity bill sent to each community member can be directly extracted from this solution. Building on this concept, we also introduce two additions to the basic algorithm, aiming to enhance the stability of the community, which a global bill minimisation may fail to ensure (e.g., very asymmetrical solutions between members may lead to some of them opting out). The first addition is the computation of the self-sufficiency rates of the community members, defined as the proportion of the electricity demand covered by local electricity, which can be exploited as a revenue sharing mechanism. The second addition is the use of initial repartition keys based on which the optimised ones are computed, so as to ensure a minimum contractual level of revenue for each of them. We have tested our methodology with a broad range of scenarios illustrating its ability to reduce the sum of the electricity bills of the community members and to share the revenues ensuring the stability of the renewable energy community. Our results show that creating a community using this methodology can potentially reduce the electricity costs for all community members, and that self-sufficiency rates and initial keys can be used to stabilise the community by performing revenue sharing among them

Optimal Control of Renewable Energy Communities with Controllable Assets

The control of Renewable Energy Communities (REC) with controllable assets (e.g batteries) can be formalised as an optimal control problem. This paper proposes a generic formulation for such a problem whereby the electricity generated by the community members is redistributed using repartition keys. These keys represent the fraction of the surplus of local electricity production (i.e electricity generated within the community but not consumed by any community member) to be allocated to each community member. This formalisation enables us to jointly optimise the controllable assets and the repartition keys, minimising the combined total value of the electricity bills of the members. To perform this optimisation, we propose two algorithms aimed at solving an optimal open-loop control problem in a receding horizon fashion. Moreover, we also propose another approximated algorithm which only optimises the controllable assets (as opposed to optimising both controllable assets and repartition keys). We test these algorithms on renewable energy community control problems constructed from synthetic data, inspired from a real-life case of REC. Our results show that the combined total value of the electricity bills of the members is greatly reduced when simultaneously optimising the controllable assets and the repartition keys (i.e the first two algorithms proposed). These findings strongly advocate the need for algorithms that adopt a more holistic standpoint when it comes to controlling energy systems such as renewable energy communities, co-optimising or jointly optimising them from both a traditional (very granular) control standpoint and a larger economic perspective