Holistic development of intelligent electricity markets using the tarot – optimized tariff – with technical, economic, regulatory, and environmen-tal models

Abstract

continuously increasing in several countries due to ongoing technical and economic breakthroughs and the implementation of incentive policies. In Brazil, the deployment of on-grid DG under a compensation scheme began in 2012, when the first specific regulation was published (Normative Resolution - REN 482). REN 482 implemented the net metering policy to boost DG installed capacity, as such a policy is highly beneficial for prosumers. However, DG installed capacity has increased substantially, causing de-cision-makers to rethink the regulatory framework and seek a more balanced solution through Ordinary Law 14300/2022 (OL). In this context, it is clear that regulatory aspects are in the spotlight in Brazil, given their importance to society and the need for changes. However, there is a lack of robust and holistic regu-latory models that can be used to implement efficient regulatory frameworks. Thus, analyses are typically empirical. In this context, this thesis fills an essential research gap by developing cutting-edge regulatory models. First, it adapts the optimized tariff model - TAROT (socioeconomic regulated electricity market model) and the Bass Diffusion Model - BDM (forecasting model of technology integration) to the context of DG and ESSs to evaluate the consequences of increasing penetration levels in the market. Second, it uses the TAROT, BDM, and Life Cycle Assessment - LCA (environmental impact analysis technique) to holistically analyze the impacts of the OL, taking into account socioeconomic and environmental indica-tors. Third, it combines the TAROT, BDM, and LCA into a multi-objective optimization (MOO) approach to obtain holistic and optimal regulatory frameworks for DG. The optimal solutions are compared to the OL to evaluate whether the law achieved a satisfactory trade-off. Fourth, it extends the proposed model by assuming the co-existence between conventional markets and Community-Based Markets (CBMs), de-fined as groups of members that share common interests, such as trading electricity from DG. Lastly, it introduces a scenario-based bi-level optimization problem to account for the random locational aspect of DG systems. Results demonstrate that the OL is successful in mitigating tariff raises and reducing social inequality. By contrast, there are negative implications to the DG business, market welfare, and the envi-ronment, as socioeconomic welfare losses at 2.12 billion R$/year or 0.42 billion US$/year, and emissions at 0.35 Mt CO2-eq/year are estimated in total for the 35 analyzed concession areas. The MOO approach indicates that the OL is a dominated or non-optimal solution since it is not located on the Pareto frontiers. Thus, while reductions in the compensation for the electricity injected into the grid are necessary in Brazil, the OL defined the compensation empirically, without the application of well-defined methods, implying a sub-optimal solution. Assuming the Euclidian knee points, the optimal solutions implied benefits of around 24% in terms of electricity tariff affordability, with small losses of roughly 6% in terms of socio-economic welfare and global warming potential. Additionally, one can conclude that CBMs can be signif-icantly beneficial in mitigating energy poverty in Brazil, as benefits of around 1.4% were estimated as-suming the whole regulated market, or 16.5% assuming only the CBMs participants. However, such ben-efits would only take place if low-income consumers could participate in the CBMs. Lastly, the bi-level problem demonstrates the importance of assuming the uncertainties associated with DG integration.