Chenchen Li

The energy affordability principles aim to enhance energy accessibility and reduce electricity expenditures for low-income communities. However, the advocated affordability policies impose new requirements on system operators, as few have been translated into technical utility models. Energy storage systems (ESS) are crucial in implementing energy affordability by regulating voltage profiles and reducing power loss. This paper proposes an energy affordability-embedded robust planning (EARP) model to relieve the energy burden of low-income loads through equitable ESS deployment. The proposed model innovatively enforces implicit energy affordability (EA) constraints into technical models with two steps. First, an energy affordability slackness component (EASC) variable is introduced to evaluate the smallest gaps between the given preliminary planning decisions and the desired affordability-satisfied decisions. Then, by relaxing the dispatch model with the EASC variable, the implicit EA constraints are converted into explicitly cutting planes using the duality theorem. The proposed bi-level EARP model is solved via an improved column & constraint generation (C&CG) algorithm. Case studies on the IEEE 33-Bus and 123-Bus systems verify the model's effectiveness in relieving energy burdens and achieving better sustainability in the long term compared with conventional bill assistance programs.

In this research work, a comprehensive incentive-based demand response (I-DR) capacity assessment tool is developed for enhancing energy flexibility and sustainability. Under I-DR, a participant’s response to load regulation activities is based on accepting appropriate financial incentives on a voluntary basis. Accordingly, the sustainability of energy and the environment can be better guaranteed through a more reasonable energy distribution among participants on a voluntary basis with clear financial incentives. It is thus meaningful to conduct an assessment for I-DR potential capacity to guide the potential of energy and environmental sustainability. This article introduces the development of an I-DR capacity assessment tool that includes three modules: a data preprocessing module, an I-DR capacity assessment module, and a visualization module, to expand the adaptability of this tool to real-world data and scenarios. A load-reduction-oriented assessment process and a carbon-reduction-oriented assessment process are also provided to expand the role of the tool to utility companies. This tool is then validated via field load data within one residential service territory of Southern Company (SOCO). Validation results demonstrate that this tool can effectively assess I-DR capacity for residential energy, which enables a user-friendly view of the quantitative relationship of the reducible load or the reducible carbon emission under the corresponding financial incentives. This tool shows promise for achieving support for decarbonization and energy sustainability.

Network reconfiguration (NR) is a highly complex combinatorial problem with discrete and nonlinear characteristics. With the expansion of the distribution network (DN), solving the NR problem faces the challenge of high dimensions. In this article, we propose a structural decomposition approach (SDA), where the NR problem is suitably allocated to three processes: partition, reconfiguration of equivalent networks, and merging. According to the loop in the original DN, the loop-oriented network partition model is proposed to divide the original DN into multiple equivalent networks, including a loop region and a compressed region. The reconfiguration model is built for the equivalent networks, and the solutions for the loop region and compressed region can be obtained. Then the merging model with the correction method is proposed to merge all reconfiguration solutions of equivalent networks, deal with the inconsistent solutions of different equivalent networks, and obtain the optimal reconfiguration solution of the original DN. Numerical case studies were conducted on the IEEE 33-bus and 119-bus DNs. Compared with other heuristic algorithms for solving NR problem, SDA reduces computation time by 70% while ensuring the optimality of NR strategies. These results demonstrate the effectiveness and exceptional performance of the proposed method.

Low-income communities have challenges obtaining equal access to electricity, so it is important to implement energy justice. Meanwhile, increasing installations of distributed generators (DGs) in distribution systems is a viable means to promote energy equity. Therefore, this work explores a new planning method to address the siting and sizing problem of DG units with an energy equity constraint embedded in the model, and provides concluding guidelines as a rule of thumb for future DG planning considering energy equity. In this paper, first, the DG siting and sizing problem is formulated as a stochastic bi-level model, where energy equity is considered as an energy burden constraint. The upper level determines the optimal sites and sizes of DGs under investment and energy burden constraints, while the lower level optimizes the distribution operation. Next, a solution method is proposed by applying the Karush-Kuhn-Tucker optimality conditions to convert the stochastic bi-level model to a single-level model. A decomposition approach and Progressive Hedging Algorithm are used to further simplify the single-level model into multiple easy-to-solve subproblems. Finally, numerical studies are performed on two test systems to verify the effectiveness of the proposed model. Technical rule-of-thumb guidelines are presented for siting and sizing DGs considering energy equity.

Distribution network reconfiguration is an effective method to face the problem of power fluctuation in the power system. Previous studies have focused on mathematical optimization techniques with complex modeling processes and heuristic algorithms with time-consuming solving processes to obtain the optimal reconfiguration strategy. In this article, a hybrid data-driven and model-based distribution network reconfiguration (HDNR) framework is proposed, where the model-based module includes model reduction and goal-oriented clustering to cluster the identical reconfiguration strategies. Here, the data-driven module is implemented through a long short-term memory network to learn the mapping mechanism between load distribution and optimal reconfiguration strategies. The model-driven module and the data-driven module are coupled through the proposed hierarchical network recovery process, which presents the reconfiguration results layer by layer. Finally, the numerical case study on the IEEE 33-bus, IEEE 119-bus, and IEEE 123-bus network shows the validity of the proposed HDNR framework. It is shown that the solution space is reduced, which contributes to reducing computation time and resources. Moreover, the obtained accuracy of the reconfiguration strategy is higher than most existing research even with limited data samples.

The electricity theft behavior is becoming more and more common in order to reduce the cost of consumption. A detection method for electricity theft based on random forest is proposed in this paper. Besides, the evaluation index system including the power consumption slope index, the line-loss index, and the warning information index is built to consider the factors affecting the electricity theft. For improving the detection accuracy, the data cleaning and miss value preprocess are conducted before training the decision trees. Finally, a numerical case and the comparison in performance with decision tree and GBDT are used to verify the availability and superiority of the proposed detection method.

Distributed energy resources (DERs) bring about challenges related to the participation of an increasing number of prosumers with strong social attributes in peer-to-peer (P2P) energy sharing markets, resulting in the increased complexity of socio-technical systems. Previous research has focused on energy sharing analysis based on rational games without considering the social attributes of prosumers, which are not typically used in real scenarios. In this paper, an interdisciplinary P2P energy sharing framework that considers both technical and sociological aspects is proposed. It is based on prospect theory (PT) and stochastic game theory, in which the prosumers work as followers with subjective load strategies, while an energy sharing provider (ESP) serves as the leader with a dynamic pricing scheme. A subjective utility model with risk utility (RU) determined by PT is designed for prosumers, and a profit model for dynamic prices is suggested for ESP. Moreover, a solution algorithm that consists of interpolation and curve fitting to obtain the RU function, the aggregation of prosumers to a Markov decision process, and a differential evolution algorithm to solve the game are proposed to solve the problems of the "curse of dimensionality" and discreteness arising from the social attributes of prosumers. Numerical analysis reveals the results of the Stackelberg equilibrium and demonstrates the effectiveness of this method in terms of the social behavior of prosumers, i.e., radicalness when losing and conservatism when gaining.