• Energy Research

This paper develops a robust chance-constrained model for handling the uncertainties of generation and consumption in multi-carrier energy hubs. The proposed model incorporates corresponding loading factors for each type of electrical, heating, and cooling loads. This is done to assess the maximum loadability of the whole system. In this respect, the chance-constrained approach is implemented for the feasibility assessment of the operation problem with uncertainties. The uncertainties which are assumed here include the forecast errors of electrical, heating, and cooling load demands, and the volatile solar power generation. The overall problem formulation is developed in the mixed-integer linear programming (MILP) framework. The standard chance-constrained approach is converted to a deterministic optimization model by utilizing the Big M method. The main objective of the proposed model is to maximize the loadability index with uncertainties while addressing the permissible risk index of the decision-maker. The studied energy hub comprises electrical, heating, and cooling loads, and the energy flow technique is adopted in this paper to model the load balance equations. The simulation results are presented for different scenarios while addressing features of the proposed model for the summer and winter seasons. Furthermore, the developed model is evaluated for different scenarios and a comparison is made with the information-gap decision theory (IGDT) method.

Short-term operation is one of the most significant aspects of power systems operation and planning and it mainly includes resource scheduling including renewable and non-renewable energy sources. The mentioned problem is modeled as an optimization problem with one or more objectives depending upon the priorities of the decision maker (system operator) and several equality and inequality constraints. In this respect, this paper proposes a short-term operational model for the hydrothermal power systems in the presence of wind power generation and pumped-hydro storage (PHS) units. The problem is formulated as a single-objective optimization problem that aims to minimize the total operating cost including the fuel cost of thermal units. The problem has been comprehensively investigated through five case studies where the first case study is used for the sake of validation. Then, the other case studies assessed the impacts of wind power generation, PHS unit, and uncertainty on the problem. The five case studies are as follows: Case 1: Short-term hydrothermal (STH) scheduling which aims to minimize the total cost. Case 2: Deterministic short-term hydrothermal-wind (STHTW) scheduling which aims to minimize the total cost. Case 3: Stochastic wind-hydro-thermal scheduling that seeks to minimize the total cost taking into consideration the uncertainty of the wind power generation. Case 4: Deterministic wind-hydro-thermal scheduling in the presence of the PHS unit with the total cost minimization as the objective function. Case 5: Stochastic wind-hydro-thermal scheduling in the presence of the PHS unit that is used to evaluate the impact of uncertainty of wind power generation with the total cost minimization as the objective function. It is noteworthy that the first three case studies have been formulated using a non-linear programming (NLP) model and solved using the CONOPT solver in GAMS. Case studies 4 and 5 have been presented using a mixed-integer non-linear programming (MINLP) model and solved using the DICOPT solver in GAMS. A sensitivity analysis has also been carried out to assess the system's performance with different loading conditions. In this respect, the total cost and PHS operation with different amounts of load demand were evaluated and the results have been presented and discussed.

This paper investigates a centralized, high-resolution and fast model for home energy management. The model is provided within mixed-integer linear programming (MILP) framework while it benefits from an open-access optimization model in Python for running the model free of charge. To minimize the electricity bill, the time-of-use (TOU) electricity tariff has been selected by the consumer to manage the daily electricity consumption. This consumer-centric home energy management system (HEMS) enhances the flexibility that can be provided by the dedicated consumers during peak periods while reducing the electricity bill of the end-users benefiting from the TOU tariff. The time resolution of home appliance scheduling is 15 minutes in this study and it is compatible with the smart metering data recording for energy consumed by the end-users. The simulation results show that the electricity bill would be considerably decreased by using the proposed self-scheduling model.

This paper presents a mixed-integer linear programming model for the maintenance scheduling of generating units in the power system. The proposed model is investigated for weekly scheduling for one year addressing the crew availability constraint. The maintenance scheduling problem is modeled as an optimization problem to determine the optimal timing for handling the technical constraints of the power generation sector. In addition, the technical constraints for optimal scheduling of the tasks, like sequential tasks and rest time of the crews have been addressed in the scheduling management framework. The weekly peak power and spinning reserve have been considered in line with the economic issues for power generation in the whole system. The historical market clearing price (MCP) and mid-term load forecasting have been considered in the developed model.

ABSTRACTDetermining the optimal operating point of generating units in the optimal combined heat and power scheduling is an economic optimization problem aimed at minimizing the total operating cos...

Smart Homes (SHs) play a crucial role in the broader context of smart cities, contributing to the overall efficiency, sustainability, and quality of life for residents. This work presents a computationally efficient mathematical model for Home Energy Management Systems (HEMS) to activate the Demand Side Management (DSM) while minimizing the energy consumption costs of SHs. The proposed model is represented as a standard Mixed-Integer Linear Programming (MILP) optimization problem. The proposed HEMS provides the optimal scheduling of home appliances and plugging time for charging Electric Vehicles (EV) at home. The main objective of this optimization problem is to minimize the electricity bills while improving the load profile of the distribution system in the context of smart cities. In this study, load management for a typical household has been studied considering several electricity tariffs in Portugal. The simulation results confirm that in the presence of the proposed HEMS, the electricity bill will be effectively reduced while consumer behavior is changed. The cost savings that can be attained through only shifting loads is 5.7 %, while it can reach 67.65 % by installing a hybrid energy system at the studied smart home. The weekly cost reduction percentage due to charging the EV at home is around 16.24 % compared to charging the EV using public charging stations regardless of the traveling time and waiting at the public charging stations.

In this paper, the model predictive control (MPC) strategy is utilized in smart homes to handle the optimal operation of controllable electrical loads of residential end-users. In the proposed model, active consumers reduce their daily electricity bills by installing photovoltaic (PV) panels and battery electrical energy storage (BEES) units. The optimal control strategy will be determined by the home energy management system (HEMS), benefiting from the meteorological and electricity market data stream during the operation horizon. In this case, the optimal scheduling of home appliances is managed using the shrinking horizon MPC (SH-MPC) and the main objective is to minimize the electricity cost. To this end, the HEMS is augmented by the SH-MPC, while maintaining the desired operation time slots of controllable loads for each day. The HEMS is cast as a standard mixed-integer linear programming (MILP) model that is incorporated into the SH-MPC framework. The functionality of the proposed method is investigated under different scenarios applied to a benchmark system while both time-of-use (TOU) and real-time pricing (RTP) mechanisms have been adopted in this study. The problem is solved using six case studies. In this regard, the impact of the TOU tariff was assessed in Scenarios 1–3 while Scenarios 4–6 evaluate the problem with the RTP mechanism. By adopting the TOU tariff and without any load shifting program, the cost is $\$ $ 1.2274 while by using the load shifting program without the PV and BEES system, the cost would reduce to $\$ $ 0.8709. Furthermore, by using the SH-MPC model, PV system and the BEES system, the cost would reduce to $\$ $ -0.282713 with the TOU tariff. This issue shows that the prosumer would be able to make a profit. By adopting the RTP tariff and without any load shifting program, the cost would be $\$ $ 1.22093 without any PV and BEES systems. By using the SH-MPC model, the cost would reduce to $\$ $ 1.08383. Besides, by adopting the SH-MPC, and the PV and BEES systems, the cost would reduce to $\$ $ 0.05251 with the RTP tariff, showing the significant role of load shifting programs, local power generation, and storage systems.