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.

The demand response (DR) program is one of the most promising components in the development of the Smart Grid. However, there are many challenges in practical operation to improve the existing and outdated system to comply with the DR programs. In Thailand, the major pain point of the office building owner in the DR program is the additional equipment, modification and operation cost of the existing equipment. Moreover, the sophisticated solution and control are other obstacles that need more measurements and data, and they make the operation difficult to work with. In this paper, we implemented a simple yet cost-effective hardware and software solution targeting an outdated air-conditioning system without voiding the warranty of the outdated equipment and without installing any additional measurements. In addition, the proposed operation is designed to be easy to operate under the equipment limitation and unskilled labor. More importantly, indoor temperature setpoint schedules during the DR event are forecasted with some public datasets to determine the capacity of the energy management system that can reduce the power consumption in the office building without an effect on the occupants’ comfort. To confirm the practicality of the proposed solution, the actual operation of the proposed solution can achieve the maximum power reduction at 19.80 kW (43.79% of the maximum power consumption) while keeping only 1 °C of difference from the typical room temperature (26–28 °C).

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 the future smart grid, Demand Side Management (DSM) will be one of the key technologies to facilitate utility companies and customers in order to achieve system optimality such as minimizing energy cost and peak-to-average ratio. While most of the prior works in the literature have reported good results in achieving system optimality, they mostly ignore considering the aspect of system fairness, especially in regards to the electricity bills of the users. More specifically previous works do not address the important problem of users' commitment and their actual consumption level when calculating the payments. In this paper, we tackle this problem and design an alternative fair billing mechanism that assures the system fairness. The proposed billing considers not only real-time consumption information of the grid but also the energy consumption level of each user. Thus, each user's bill will be adjusted based on their commitment and contribution to the system. Simulation results show that the proposed billing mechanism ensures fairness in users' payments.

In multi-cell cellular system, users suffer from inter-cell interference coming from neighboring cells, particularly at cell boundary. Base station (BS) cooperation is a promising technique to mitigate inter-cell interference for users located at cell-edge area and currently under discussion for further releases of the 3GPP Long Term Evolution (LTE) system. In this paper, using BS cooperation approach, we propose an inter-cell interference coordination technique to mitigate the intercell interference in multi-cell cellular network. Coordinating BSs cooperatively choose their antenna weights based on requested information from users to compromise between reducing the inter-cell interference to neighboring cell-edge users and maximizing received signal power to its own users to achieve maximum network sum-rate. Simulation results show that average sum-rate of multi-cell cellular system is increased by our proposed method particularly when users are located at cell-edge area.

This paper presents SEAMS (Solar Energy Aggregator Management System), an optimization-based framework for managing solar energy trading in smart communities under Thailand’s regulatory constraints. A major challenge is the prohibition of residential grid feed-in, which limits the use of conventional peer-to-peer energy models. Additionally, fixed pricing is required to ensure simplicity and trust among users. SEAMS coordinates prosumer and consumer households, a shared battery energy storage system (BESS), and a centralized aggregator (AGG) to minimize total electricity costs while maintaining financial neutrality for the aggregator. A mixed-integer linear programming (MILP) model is developed to jointly optimize PV sizing, BESS capacity, and internal buying price, accounting for Time-of-Use (TOU) tariffs and local policy limitations. Simulation results show that a 6 kW PV system and a 70–75 kWh shared BESS offer optimal performance. A 60:40 prosumer-to-consumer ratio yields the lowest total cost, with up to 49 percent savings compared to grid-only systems. SEAMS demonstrates a scalable and policy-aligned approach to support Thailand’s transition toward decentralized solar energy adoption and improved energy affordability.