Blockchain based energy transactions for a prosumer community

PhD thesis in Information technologyIntegration of solar micro-generation capabilities in domestic contexts is on the rise, leading to the creation of prosumer communities who generate part of the energy they consume. Prosumer communities require a decentralized, transparent and immutable transaction system in order to extract value from their surplus energy generation and usage flexibility. The aim of this study is to develop frameworks and methods to create such a prosumer transaction system with self enforcing smart contracts to facilitate trading of energy assets such as electricity units, energy flexibility incentives and storage credits. Blockchain is a transparent, distributed ledger for consensus based transaction processing maintained by a network of peer nodes. Hyperledger Fabric is a blockchain platform that offers the added benefits of lower operating cost, faster transaction processing, user authentication based access control and support for self enforcing smart contracts. This thesis investigates the applicability of Hyperledger Fabric to tokenize and transact energy assets in a unified transaction system. Data driven approaches to implement an incentive based energy flexibility system for peak mitigation on the blockchain are also investigated. To this end, the stakeholders for such a transaction management system were identified and their business relationships and interactions were described. Energy assets were encapsulated into blockchain tokens and algorithms were developed and encoded into self enforcing smart contracts based on the stakeholder relationships. A unified transaction framework was proposed that would bring on board all the stakeholders, their trading relationships and the assets being transacted. Tokens and methods in the transaction system were implemented in fungible and non fungible versions and the versions were critically compared in terms of application area, design, algorithmic complexity, performance, advantages and disadvantages. Further, with a focus on energy flexibility applications, a prosumer research dataset was analysed to gain insights into the production and consumption behaviors. Based on these insights, a data driven approach for peak mitigation was proposed and implemented on the Hyperledger Fabric blockchain. The thesis thus addresses different aspects of a blockchain based prosumer transaction system, and shows the feasibility of proposed approaches through implementation and performance testing of proofs of concept

Peak-Load Reduction by Coordinated Response of Photovoltaics, Battery Storage, and Electric Vehicles

Peak-load management is an important process that allows energy providers to reshape load profiles, increase energy efficiency, and reduce overall operational costs and carbon emissions. This paper presents an improved decision-tree-based algorithm to reduce the peak load in residential distribution networks by coordinated control of electric vehicles (EVs), photovoltaic (PV) units, and battery energy-storage systems (BESSs). The peak-load reduction is achieved by reading the domestic load in real time through a smart meter and taking appropriate coordinated action by a controller using the proposed algorithm. The proposed control algorithm was tested on a real distribution network using real load patterns and load dynamics, and validated in a laboratory experiment. Two types of EVs with fast and flexible charging capability, a PV unit, and BESSs were used to test the performance of the proposed control algorithm, which is compared with that of an artificial-neural-network technique. The results show that using the proposed method, the peak demand on the distribution grid can be reduced significantly, thereby greatly improving the load factor

Prioritized experience replay based deep distributional reinforcement learning for battery operation in microgrids

This is the author accepted manuscript. The final version is available on open access from Elsevier via the DOI in this recordData availability: Data will be made available on request.Reinforcement Learning (RL) provides a pathway for efficiently utilizing the battery storage in a microgrid. However, traditional value-based RL algorithms used in battery management focus on formulating the policies based on the reward expectation rather than its probability distribution. Hence the scheduling strategy is solely based on the expectation of the rewards rather than the distribution. This paper focuses on scheduling strategy based on probability distribution of the rewards which optimally reflects the uncertainties in the incoming dataset. Furthermore, the prioritized experience replay samples of the training experience are used to enhance the quality of the learning by reducing bias. The results are obtained with different variants of distributional RL algorithms like C51, Quantile Regression Deep Q-Network (QR-DQN), Fully Quantizable Function (FQF), Implicit Quantile Networks (IQN) and rainbow. Moreover, the results are compared with the traditional deep Q-learning algorithm with prioritized experienced replay. The convergence results on the training dataset are further analyzed by varying the action spaces, using randomized experience replay and without including the tariff-based action while enforcing the penalties for violating battery SoC limits. The best trained Q-network is tested with different load and PV profiles to obtain the battery operation and costs. The performance of the distributional RL algorithms is analyzed under different schemes of Time of Use (ToU) tariff. QR-DQN with prioritized experience replay has been found to be the best performing algorithm in terms of convergence on the training dataset, with least fluctuation in validation dataset and battery operations during different tariff regimes during the day.European Regional Development Fun

The benefits of distributed battery energy storage systems for customers and network operators based on measured data from deployed systems

The rapid growth in distributed energy resources such as solar photovoltaics and battery energy storage systems (BESS) has introduced unprecedented opportunities and challenges for the electricity sector. Although these systems increase the self-consumption of the customer, their integration requires further investment into the electricity network, and they reduce the revenue of the network operators. Conversely, there are opportunities to engage distributed BESS to perform multiple grid services that may benefit all customers and the network operator. Out of these services, the reduction of network demand peaks and photovoltaic export peaks are highly valued in the electricity sector. Currently, there is a lack of literature on detailed analysis of deployed, non-coordinated residential BESS and assessment of co-optimisation of commercial-scale BESS to maximise the financial outcomes for both the customers and the network operator. This thesis explores these gaps in the literature in the following contexts: i) the ability of residential BESS to reduce demand during network peaks, ii) the ability of residential BESS to reduce photovoltaic export peaks, and iii) the ability of commercial-scale BESS to be co-optimised for both the customers and the network operator. Analysing the measured data from deployed systems showed that on average, residential BESS that were operating as expected were already discharging to support the network during demand peaks but were unable to reduce photovoltaic export peaks because they were charged to full prior to the peak period. Different operational strategies for the BESS were modelled and it was found that setting a 27% limit on the batteries’ charging power can increase their export peak reduction from almost 0% to 15%, with no impact on their ability to increase self-consumption. A co-optimisation model was developed for a scenario of maximising the financial outcomes of installing commercial-scale BESS for both customers and the network operator. Although the outcomes are site-specific, the model could be used to assess the outcomes at different locations and under different assumptions. Taken together these findings should provide knowledge and methods that will contribute to the uptake of behind the meter BESS in a way that benefits all customers and the network operator