Improving distributed renewable energy performance and urban sustainability with virtual power plants

With the rapid urbanisation in the past few decades, our urban environment faces a great dilemma of population growth, carbon emissions, energy scarcity and economic difficulties. In recent years, distributed renewable energy (DRE) has increasingly been considered the solution for improving urban sustainability because of its reduced environmental impact and lower capital cost. However, the adoption of DRE has been significantly limited due to its low efficiency, unstable supply and its potential impact on the public grid. With the basic concept of replacing centralised large-scale power plants with a network of many distributed energy resources (DERs), virtual power plants (VPPs) can increase the market competitiveness of distributed renewables. VPPs have been gaining market size and attracting research interest in recent years. However, understanding of their management in a complex urban environment and their impact on urban sustainability is not well developed. VPPs are usually operated in an environment full of complexities and uncertainties caused by the changeable output of renewable energy generators, flexible user load and fluctuating electricity market conditions. Understanding the interaction between VPP systems and the urban environment is essential to evaluate their impacts on economic, environmental, and social aspects. However, based on the literature review for this research, most previous studies of VPPs have focussed mainly on the systems and technology to investigate the uncertainty of capturing methods and optimisation algorithms, and assessments of the implementation and the impact of VPPs in the urban environment are rarely found. Based on a scientometric literature review, this study proposes a research framework for the conduct of VPP research to address urban sustainability concerns. Following the framework, a case study is carried out in an Australian city with a complex urban environment and socioeconomic profile. Multiple methodologies, including deep learning-based image recognition, geoprocessing, and supervised learning demand data mining, are applied to characterise the local demand-supply conditions and urban dynamics such as the angular parameters of buildings and the impact of shading. A simulation model of a VPP combined with renewable generators and storage is established. To address the complexities and uncertainties involved in the case study city, a reinforcement learning-based control and optimisation strategy is developed and validated, which is capable of optimising urban VPP performance with greater efficiency and robustness. Finally, the impacts of VPP on urban sustainability are evaluated from the perspectives of economic viability, energy performance, reduction of carbon emissions and socioeconomic effects on local communities. Detailed data analysis and discussion are provided, and suggestions for future development and policy-making are made. A VPP design based on the research framework shows overall good performance in reducing carbon emissions, lowering the cost of electricity generation and reducing the price to users. The VPP’s energy and economic performance vary for different user groups with diverse demand/supply conditions and socioeconomic conditions. Based on the results of this research, a knowledge-sharing web platform is established, which has the potential to benefit local community members as well as local stakeholders in the renewable energy sector and provides easy access to the research data and findings. The outcome of this research provides better understanding of the potential of VPPs to improve urban sustainability and of how VPP systems can evolve with the Australian urban environment. It will also provide guidance for local authorities on the better management of distributed renewable resources and the implementation of VPP projects.