• Energy Research

The widespread adoption of Electric Vehicles (EVs) in the smart grid is transforming the traditional grid into a more complex system. EVs have the ability to both charge and discharge, acting as loads that draw high power and sources that inject energy back into the grid. Consequently, energy sharing and management within smart grid communities integrated with EVs have become interesting aspects to study in order to efficiently utilize this energy. However, most existing research focuses solely on energy sharing within single communities, utilizing homogeneous energy profiles and neglecting the potential of heterogeneous energy across multiple communities. EVs also possess the capability to travel to different places and communities, where they can engage in energy sharing with areas that have varying load profiles and prices. In this work, a novel three-level energy sharing management approach is proposed for a multiple community system integrating movable energy storage such as EVs. This model involves three main entities: the Utility Company (UC), Community Energy Aggregator (CEA), and EVs. The energy sharing problem is formulated as a Stackelberg game, with all entities striving to maximize their utility through optimal strategies, including pricing, energy demand, or supply. The proposed model is validated through comparison with typical human charging behavior, as well as single- and multiple-community two-level game models. The findings reveal that the proposed model successfully optimizes pricing and energy strategies, significantly lowering the peak-to-average ratio and smoothing the overall energy profile.

The integration of renewable resources into the existing power distribution system is expanding to reduce gas emissions, treat climate change and satisfy the current global need for clean energy. If the location and size of these renewable generators are determined without considering uncontrollable reactive power compensation caused by their intermittent nature, the resultant power system may suffer from system instability and decreased reliability. Therefore, the issue of optimal location and size of renewable resources attracts great attention. In this paper, a methodology is proposed to optimize the locations and capacities of distributed renewable generators installed in conventional power distribution systems. In particular, uncontrollable reactive power compensation of these renewable resources is considered in this paper and managed through the proposed methodology to ensure power system reliability and stability. As a result, the proposed methodology reminds us of the importance of reactive power compensation by performing better in power losses reduction and the robustness of voltage stability against variable reactive power compensation.

In today’s power systems, electric vehicles (EVs) constitute a significant factor influencing electricity dynamics, with their important role anticipated in future smart grid systems. An important feature of electric vehicles is their dual capability to both charge and discharge energy to/from their battery storage. Notably, the discharge capability enables them to offer vehicle-to-grid (V2G) services. However, most V2G research focuses on passenger cars, which typically already have their own specific usage purposes and various traveling schedules. This situation may pose practical challenges in providing ancillary services to the grid. Conversely, electric school buses (ESBs) exhibit a more predictable usage pattern, often deployed at specific times and remaining idle for extended periods. This makes ESBs more practical for delivering V2G services, especially when prompted by incentive price signals from grid or utility companies (UC) requesting peak shaving services. In this paper, we introduce a V2G energy sharing model focusing on ESBs in various schools in a single community by formulating the problem as a leader–follower game. In this model, the UC assumes the role of the leader, determining the optimal incentive price to offer followers for discharging energy from their battery storage. The UC aims to minimize additional costs from generating energy during peak demand. On the other hand, schools in a community possessing multiple ESBs act as followers, seeking the optimal quantity of discharged energy from their battery storage. They aim to maximize utility by responding to the UC’s incentive price. The results demonstrate that the proposed model and algorithm significantly aid the UC in reducing the additional cost of energy generation during peak periods by 36% compared to solely generating all electricity independently. Furthermore, they substantially reduce the utility bills for schools by up to 22.6% and lower the peak-to-average ratio of the system by up to 9.5%.

In the smart grid, residential consumption scheduling in demand-side management (DSM) is one of the key technologies to facilitate utility companies and users in order to achieve systems optimality such as minimizing energy cost and demand peak. The success of DSM implementation depends on the level of user participation. While most of the prior works on DSM have reported good optimal results, they show a lack of focus towards user-centric issues such as user preferences, consumption deviation, and system fairness. Failure to account for such issues may lead to lower user participation in DSM programs. To address this problem, we propose user-centric consumption scheduling and fair billing mechanism for DSM program which consider economic as well as comfort aspects. First, a user’s discomfort cost is integrated into price incentives for determining consumption schedules. Second, consumption rescheduling mechanism is designed to allow users to change their preferences if necessary, and request new schedules. Finally, to improve the level of system fairness and avoid strategic players who try to manipulate the consumption profile for their benefit, a fair billing mechanism is proposed at the end of the scheduling period which takes into account both rescheduling users and user’s consumption deviation level. Simulation results show the effectiveness of the proposed method in terms of energy cost saving and improving fairness in the user’s billing.

In today’s power system landscape, renewable energy (RE) resources play a pivotal role, particularly within the residential sector. Despite the significance of these resources, the intermittent nature of RE resources, influenced by variable weather conditions, poses challenges to their reliability as energy resources. Addressing this challenge, the integration of an energy storage system (ESS) emerges as a viable solution, enabling the storage of surplus energy during peak-generation periods and subsequent release during shortages. One of the great challenges of ESSs is how to design ESSs efficiently. This paper focuses on a distributed power-flow system within a smart home environment, comprising uncontrollable power generators, uncontrollable loads, and multiple energy storage units. To address the challenge of minimizing energy loss in ESSs, this paper proposes a novel approach, called energy-efficient storage capacity with loss reduction (SCALE) scheme, that combines multiple-load power-flow assignment with a load-shifting algorithm to minimize energy loss and determine the optimal energy storage capacity. The optimization problem for optimal energy storage capacity is formalized using linear programming techniques. To validate the proposed scheme, real experimental data from a smart home environment during winter and summer seasons are employed. The results demonstrate the efficacy of the proposed algorithm in significantly reducing energy loss, particularly under winter conditions, and determining optimal energy storage capacity, with reductions of up to 11.4% in energy loss and up to 62.1% in optimal energy storage capacity.

The rapid advancement in technology and rise in energy consumption have motivated research addressing Demand-Side Management (DSM). In this research, a novel design for Home Energy Management (HEM) is proposed that seamlessly integrates Battery Energy Storage Systems (BESSs), Photovoltaic (PV) installations, and Electric Vehicles (EVs). Leveraging a Mixed-Integer Linear Programming (MILP) approach, the proposed system aims to minimize electricity costs. The optimization model takes into account Real-Time Pricing (RTP) tariffs, facilitating the efficient scheduling of household appliances and optimizing patterns for BESS charging and discharging, as well as EV charging and discharging. Both individual and multiple Smart Home (SH) case studies showcase noteworthy reductions in electricity costs. In the case of multiple SHs, a remarkable cost reduction of 46.38% was achieved compared to a traditional SH scenario lacking integration of a PV, BESS, and EV.

Accurate electricity demand forecasting serves as a vital planning tool, enhancing the reliability of management decisions. Apart from that, achieving these aims, particularly in managing peak demand, faces challenges due to the industry’s volatility and the ongoing increase in residential energy use. Our research suggests that employing deep learning algorithms, such as recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), holds promise for the accurate forecasting of electrical energy demand in time series data. This paper presents the construction and testing of three deep learning models across three separate scenarios. Scenario 1 involves utilizing data from all-day demand. In Scenario 2, only weekday data are considered. Scenario 3 uses data from non-working days (Saturdays, Sundays, and holidays). The models underwent training and testing across a wide range of alternative hyperparameters to determine the optimal configuration. The proposed model’s validation involved utilizing a dataset comprising half-hourly electrical energy demand data spanning seven years from the Electricity Generating Authority of Thailand (EGAT). In terms of model performance, we determined that the RNN-GRU model performed better when the dataset was substantial, especially in scenarios 1 and 2. On the other hand, the RNN-LSTM model is excellent in Scenario 3. Specifically, the RNN-GRU model achieved an MAE (mean absolute error) of 214.79 MW and an MAPE (mean absolute percentage error) of 2.08% for Scenario 1, and an MAE of 181.63 MW and MAPE of 1.89% for Scenario 2. Conversely, the RNN-LSTM model obtained an MAE of 226.76 MW and an MAPE of 2.13% for Scenario 3. Furthermore, given the expanded dataset in Scenario 3, we can anticipate even higher precision in the results.