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This work 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.

This work investigates a centralized, high-resolution, and fast model for home energy management. The model is provided within the 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 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 article presents a robust chance-constrained optimization framework for the optimal operation management of an energy hub (EH) in the presence of electrical, heating, and cooling demands, and renewable power generation. The proposed strategy can be used for optimal decision making of operators of EHs or energy providers. The electrical energy storage device in the studied EH can handle the fluctuations in operating points raised by such uncertainties. In order to model the hourly demands and renewable power generation uncertainties, a robust chance-constrained close-to-real-time model is adopted in this article. The considered EH in this study follows a centralized framework and the EH operator is responsible for the optimal operation of the hub assets based on the day-ahead scheduling. A thorough analysis of energy flows with different carriers is presented. In addition, a numerical stability test regarding the selection of the time step size is performed to guarantee the solution's time resolution independence, occurring in previous studies.

Recently, renewable energy resources and their impacts have sparked a heated debate to resolve the Australian energy crisis. There are many projects launched throughout the country to improve network security and reliability. This paper aims to review the current status of different renewable energy resources along with their impacts on society and the environment. Besides, it provides for the first time the statistics of the documents published in the field of renewable energy in Australia. The statistics include information such as the rate of papers published, possible journals for finding relative paper, types of documents published, top authors, and the most prevalent keywords in the field of renewable energy in Australia. It will focus on solar, wind, biomass, geothermal and hydropower technologies and will investigate the social and environmental impacts of these technologies.

This paper presents a pool trading model within a local energy community considering home energy management systems (HEMSs) and other consumers. A transparent mechanism for market clearing is proposed to incentivise active prosumers to trade their surplus energy within a rule-based pool market in the local energy community. A price-based demand response program (PBDRP) is considered to increase the consumers’ willingness to modify their consumption. The mathematical optimization problem is a standard mixed-integer linear programming (MILP) problem to allow for rapid assessment of the trading market for real energy communities which have a considerable number of consumers. This allows for novel energy trading strategies amongst different clients in the model and for the integration of a pool energy trading model at the level of the local energy community. The objective function of the energy community is to minimize the overall bills of all participants while fulfilling their demands. Two different scenarios have been evaluated, independent and integrated operation modes, to show the impacts of coordination amongst different end-users. Results show that through cooperation, end-users in the local energy community market can reduce the total electricity bill. This is shown in a 16.63% cost reduction in the independent operation and a 21.38% reduction in the integrated case. Revenues for active consumers under coordination increased compared to independent operation of the HEMS.

The chapter develops a dynamic biobjective model for the generation expansion planning together with the transmission system expansion planning. A virtual database-supported nondominated sorting genetic algorithm, known as “VDS-NSGA-II,” is designed to tackle the multiyear multiobjective dynamic generation and transmission expansion planning (MMDGTEP) framework. The MMDGTEP is formulated as a biobjective optimization problem in this chapter, while the objective functions are defined as total cost minimization and also minimizing the expected energy not supplied (EENS) at the hierarchy level II, known as EENSHL-II. The first objective function is comprised of the investment and operating costs. The proposed hybrid model is decomposed into two programming problems: master problem and slave problem. In the first level, that is the master level, a virtual mapping procedure (VMP) is incorporated in the VDS-NSGA-II to evaluate the contrast of each capacity additions in the planning horizon. In the second level, that is the slave problem, a linear programming approach is employed to assess the objectives of the problem. The virtual database helps reduce the computational burden. By avoiding the monotonous calculation in the proposed framework, the convergence time is reduced, appropriately. After obtaining the optimal Pareto set, the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) decision maker is used to pick the most desired Pareto-optimal solution. The presented long-term planning model is simulated on a test power system to verify the effectiveness and efficiency of the framework. Post-print / Final draft

Determining 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...

In recent years, phasor measurement units (PMUs) as vital elements have been widely increased in control, monitoring and protection of power systems. In practice, as the size of a power system is large, it is not possible to install all PMUs over a short period of time mainly due to the financial and technical barriers. One solution would be installing the PMUs over different stages. Accordingly, the paper presents an effective approach for multistage PMU placement (MSPP) in power systems, called dynamic MSPP. Furthermore, since the probabilistic concept of observability reflects a more realistic image of power system observability compared to deterministic ones, this paper, unlike most of the existing MSPP models, investigates the MSPP model in both probabilistic and deterministic frameworks. Compared to the existing approaches and results, the obtained ones in this paper show a considerable improvement in the observability level during PMU installation period. In the proposed approach, PMUs are installed at intermediate stages aimed at maximizing the cumulative network observability in a single optimization process, instead of several subsidiary optimizations in conventional approaches. Briefly, the proposed approach offers a complete search space for the problem, while the existing models lead to limited ones. Moreover, because of the nonlinearity posed by the probabilistic concept of observability as well as the proposed MSPP, cuckoo search optimization algorithm is used to handle the complexity and a new problem encoding/decoding technique for the proposed MSPP is utilized. Eventually, the suggested framework is implemented on different case studies as well as Iranian Transmission Network to reveal the performance of the presented model.

During recent decades with the power system restructuring process, centralized energy sources are being replaced with decentralized ones. This phenomenon has resulted in a novel concept in electric power systems, particularly in distribution systems, known as Distributed Generation (DG). On one hand, utilizing DG is important for secure power generation and reducing power losses. On the other hand, widespread use of such technologies introduces new challenges to power systems such as their optimal location, protection devices' settings, voltage regulation, and Power Quality (PQ) issues. Another key point which needs to be considered relates to specific DG technologies based on Renewable Energy Sources (RESs), such as wind and solar, due to their uncertain power generation. In this regard, this paper provides a comprehensive review of different types of DG and investigates the newly emerging challenges arising in the presence of DG in electrical grids.

The global warming problem together with the environmental issues has already pushed the governments to replace the conventional fossil-fuel vehicles with Electric Vehicles (EVs) having less emission. This replacement has led to adding a huge number of EVs with the capability of connecting to the grid. It is noted that the presence of such vehicles may introduce several challenges to the electrical grid due to their Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) capabilities. In between, the Power Quality (PQ) issues would be the main items in electrical grids highly impacted by such vehicles. Thus, this paper is devoted to investigating and reviewing the challenges brought to the electrical networks by EVs. In this regard, the current and future conditions of EVs along with the recent research works made into the issue of EVs have been discussed in this paper. Accordingly, the problems due to the connection of EVs to the electrical grid have been discussed, and some solutions have been proposed to deal with these challenges.

In this paper, a stochastic optimization model is developed for optimal operation of the active distribution networks. The proposed model is investigated on the transactive energy market in the presence of active consumers, local photovoltaic power generations and storage devices. The stochastic behavior of photovoltaic panel power generation units and load consumptions have been modeled using scenario generations and scenario reduction technique. Besides, the stochastic nature of the demand power, as well as rooftop photovoltaic panels, have been investigated in this paper. In the transactive energy market model, the distribution system operator is the main responsible for the market-clearing mechanisms and controlling the net power exchange between the distribution network and upstream grid. The proposed model is tested and verified on a radial medium voltage distribution network with 16 buses.

System flexibility has been introduced as one of the most significant concepts in energy systems, and accordingly it has captured attention. It should be noted that various parameters and equipment, directly and indirectly, affect system flexibility, among which, demand response (DR) programs, distributed energy resources (DERs), and storage systems, are some important examples. In this respect, a comprehensive review of DR and integrated demand response (IDR) programs has been conducted in this chapter, and the impact of such programs on enhancing the flexibility of local energy systems has been thoroughly investigated. The local energy systems, studied in this chapter, include three residential, commercial, and industrial energy hubs, located in a 33-bus network, equipped with renewable energy sources (RES), as well as electrical and thermal energy storage systems. It should be noted that to evaluate the flexibility of the system, the operation problem of energy hubs has been investigated through simulating six different case studies, and the impact of DR/IDR programs, energy storage systems, RESs, and operation mode has been evaluated on operating costs, emissions, and flexibility. The results showed that each of the hubs will have a different reaction to the presence/absence of the mentioned items.

This paper presents the optimal operation strategy for home energy management system (HEMS) in the presence of the inverter-based heating, ventilation and air conditioning (HVAC) system. The main target of this paper is to find the optimal scheduling of the home appliances in line with the optimal operation of the air conditioner system to reduce the daily bills while the end-users discomfort index would be minimized. In this paper, the mathematical formulation is represented in mixed-integer linear programming (MILP) framework to reduce the computational burden and easily be adapted by hardware for implementation. The HEMS is the main responsible for optimal scheduling of controllable and interruptible loads as well as serving the fixed loads. The electricity tariff is based on time-of-use (TOU) mechanism and there are three different tariffs have been considered during the daily consumptions. The simulation results for the daily operation of a residential home confirms that the proposed model can effectively reduce the electricity bill while the consumer predefined comfort level is appropriately maintained

Self-scheduling of Home Energy Management Systems (HEMS) is one of the most interesting problems for active end-users to reduce their electricity bills. The electricity bill reduction by adopting Demand Response Programs (DRP) considering the flexibility of the end-users is addressed in this paper. The problem is addressed as a multi-objective optimization problem. The first objective function is the minimization of the daily bill, while the second objective aims to minimize the Discomfort Index (DI) regarding shifting the home appliances plugging-in time. The Time-of-Use (ToU) tariff is adopted in this paper and therefore, the end-users can benefit from shifting their flexible loads from peak hours to the off-peak hours and this reduces their bills, accordingly. In this case, the end-users have to change their energy consumption which imposes a level of discomfort on the end-users. Therefore, a two-stage model is proposed in this paper to deal with the mentioned objective functions. The proposed model is represented as standard mixed-integer linear programming (MILP) and for solving this problem the epsilon-constraint method is adopted in this study. The obtained Pareto front from the epsilon-constraint multi-objective framework is fed to the fuzzy satisfying method for final plan selection. These results show that by providing the Pareto set of optimal solutions to the user, they are more informed and can make decisions that better suit their preferences.

A novel linear approach for Phasor Measurement Units (PMUs) placement is proposed in this paper to ensure the topological observability taking into consideration the impacts caused by the adjacent zero-injection buses (ZIBs). The observability analysis in the paper is performed utilizing merely the main topological observability rules. Furthermore, the model is developed to include all possible single contingencies both on the lines and PMUs even where adjacent ZIBs are highly interconnected. To this end, the common approach for handling contingencies is enhanced. Moreover, the presented framework incorporates a subsidiary objective, i.e. maximizing the measurement redundancy among the set of solutions with the minimum number of PMUs beside the main objective, i.e. minimizing the number of the required PMUs. In this regard, a unified Integer Linear Programming (ILP) is used to solve the proposed optimisation problem. It is worth noting that the suggested approach is applied to different test systems like IEEE 14-bus, 30-bus, 39-bus, 57-bus as well as 118-bus system to validate the effectiveness and efficiency of the proposed framework and the obtained results are compared to those reported by the prior researches.

Wind energy is one of the most important sources of energy in the world. In recent decades, wind as one of the massive marine energy resources in the ocean to produce electricity has been used. This chapter introduces a comprehensive overview of the efficient ocean wind energy technologies, and the global wind energies in both offshore and onshore sides are discussed. Also, the classification of global ocean wind energy resources is presented. Moreover, different components of a wind farm offshore as well as the technologies used in them are investigated. Possible layouts regarding the foundation of an offshore wind turbine, floating offshore, as well as the operation of wind farms in the shallow and deep location of the ocean are studied. Finally, the offshore wind power plant challenges are described.

The paper presents the info-gap theory for the sake of developing a robust framework for short-term hydrothermal scheduling to tackle severe load uncertainty. Deploying this method, the system operator is provided with a robust decision-making strategy to guarantee the minimum cost under load variation condition while practical and technical limitations such as dynamic ramp rate are taken into consideration. For this purpose, the proposed Unit Commitment (UC) problem considering all above advantages would be modeled in a linear framework, which is in turn taken into account as another outstanding feature of this study as it is compatible to apply to real-world systems. In order to investigate the model efficiency, the modified version of the IEEE 118-bus test system having 54 thermal beside 8 hydro plants is chosen as the case study. Eventually, the results demonstrate how demand fluctuations and errors in the predicted load can be tolerated by allocating additional robust cost.

This paper investigates a fully decentralized model for electricity trading within a transactive energy market. The proposed model presents a peer-to-peer (P2P) trading framework between the clients. The model is incorporated for industrial, commercial, and residential energy hubs to serve their associated demands in a least-cost paradigm. The alternating direction method of multipliers (ADMM) is implemented to address the decentralized power flow in this study. The optimal operation of the energy hubs is modeled as a standard mixed-integer linear programming (MILP) optimization problem. The corresponding decision variables of the energy hubs operation are transferred to the peer-to-peer (P2P) market, and ADMM is applied to ensure the minimum data exchange and address the data privacy issue. Two different scenarios have been studied in this paper to show the effectiveness of the electricity trading model between peers, called integrated and coordinated operation modes. In the integration mode, there is no P2P energy trading while in the coordinated framework, the P2P transactive energy market is taken into account. The proposed model is simulated on the modified IEEE 33-bus distribution network. The obtained results confirm that the coordinated model can efficiently handle the P2P transactive energy trading for different energy hubs, addressing the minimum data exchange issue, and achieving the least-cost operation of the energy hubs in the system. The obtained results show that the total operating cost of the hubs in the coordinated model is lower than that of the integrated model by $590.319, i.e. 11.75 % saving in the costs. In this regard, the contributions of the industrial, commercial, and residential hubs in the total cost using the integrated model are $3441.895, $596.600, and $988.789, respectively. On the other hand, these energy hubs contribute to the total operating cost in the coordinated model by $2932.645, $590.155, and $914.165 respectively. The highest decrease relates to the industrial hub by 14.8 % while the smallest decrease relates to the residential hub by 1 %. Furthermore, the load demand in the integrated and coordinated models is mitigated by 13 % and 17 %, respectively. These results indicate that the presented framework could effectively and significantly reduce the total load demand which in turn leads to reducing the total cost and power losses.

The issue of environmental emissions has forced the power systems to use cleaner energy sources such as renewable and hydroelectric technologies. However, during recent decades due to the limitations on the available water in many regions, the optimal water reservoir usage has been highlighted. In this regard, this paper proposes a multi-objective model for short-term hydrothermal scheduling problem in the presence of the pumped-storage technology. It is noted that the framework well models the cascaded configuration of hydro reservoirs. Besides, in order to more accurately model the mentioned problem, a Mixed-Integer Non-Linear Programming (MINLP) optimization framework is presented. In this respect, the valve-loading effects occurred in thermal power generation technologies have been taken into account which turns the existing convex optimization problem into a non-convex one. In order to solve the mentioned problem, the Normal Boundary Intersection (NBI) method has been used while the VIKOR decision maker is employed to choose the most compromise solution amongst the Pareto optimal solutions obtained by NBI method. Finally, the efficiency of the proposed model has been verified through implementing four case studies and comparing the obtained results with those obtained by different methods.

This paper presents a new model for the self-scheduling problem using a home energy management system (HEMS), considering the presence of solar photovoltaic (PV) panels and an air conditioner (AC). The AC used in this study utilizes an inverter for controlling the temperature. The proposed model adopts the time-of-use (TOU) tariff for electricity, and minimization of the daily bill is the main objective of the proposed model. In the proposed model, there are some fixed and flexible loads, and optimal scheduling of the home appliances is modeled as a mixed-integer linear programming (MILP) problem to achieve the minimum daily bill. The HEMS in this paper incorporates a PV system, combined with the electrical energy storage (EES) system to deal with the uncertain solar power generation as well as optimally serving the loads during the peak hours, taking into account the charging and discharging control strategy of the installed EES. The indoor temperature can be held within a predefined margin and the settings of the AC system may be obtained according to the indoor-outdoor temperature model, which has been presented in this paper. During peak hours, the contribution of the AC in energy consumption to the daily bill is considerable. Therefore, optimal operation of the AC and other home appliances can effectively reduce the energy consumption and consequently, will result in reduced bills.

This paper proposes a novel method for solving the Optimal Power Flow (OPF) problem in conditions close to realtime. The linearized cost function of the generating units is used to this end. Besides, the presented linear model is solved using the Consensus Alternating Direction Method of Multipliers (C-ADMM) approach. This technique would provide the possibility of modeling the problem both in centralized and decentralized manners. The suggested method exploits the power flow results obtained from the previous iteration to considerably improve the rate of convergence. As the C-ADMM method uses an iterative technique, Lagrange multipliers, and the norm function, the rate of convergence highly depends upon assigning the initial conditions and the optimality gap. Thus, using the operating points of the previous instant due to being close to the operating point of the current instant would enhance the results. The proposed model has been implemented on two case studies including the Pennsylvania-New Jersey-Maryland (PJM) network to verify the results and the 9-bus system to evaluate the performance of the model for the daily operation.

This paper demonstrates day-ahead operation of power systems in the presence of a Demand Response Program (DRP) for serving exact amounts of demanded energy over the operational horizon. The proposed two-stage model features a here-and-now framework for shaping the aggregated demands during operation. First, the day-ahead scheduling problem is solved by adopting Unit Commitment (UC) to determine the generation level of power generation units as well as the Locational Marginal Prices (LMPs). Afterwards, the obtained LMPs are considered as the Time of Use (ToU) for the second step of the scheduling and reshaping the demanded loads of each aggregator. A new methodology is provided in this paper to estimate the reaction of consumers behavior in terms of encouraging their participation in DRPs. Unlike classical models which adopt load reduction over the operational horizon, this model ensures that the total demanded loads will be served. Therefore, the total supplied energy for the operational period before and after DRP implementation remains unchanged. Meanwhile, the total payment of consumers will be considerably reduced by adopting this strategy. The simulation results on the 6-bus test system clarify that the proposed model can reduce the total operational cost as well as smoothen the load profile and nodal prices over the operational horizon.

This paper presents an operation strategy of energy hubs in the presence of electrical, heating, and cooling demand as well as renewable power generation uncertainties. The proposed strategy can be used for optimal decision makingof energy providers companies, as well as, other private participants of hub operators. The presence of electrical energy storage devise in the assumed energy hub can handle the fluctuations in the operating points raised by suchuncertainties. In order to modeling of hourly demands and renewable power generation uncertainties a scenario generation model is adopted in this paper. The considered energy hub in this study follows a centralized framework andthe energy hub operator is responsible for optimal operation of the hub assets based on the day-ahead scheduling. The simulation result illustrates that in the presence of electrical energy storage devices the optimal operation of hub assets can be attained.

This paper investigates the optimal allocation of Spinning Reserve (SR) for power systems in the presence of Renewable Energy Sources (RES) and Electrical Energy Storage (EES) devices. This is done in order to reduce the system’s dependency on thermal generation units and the decrease total daily operational cost. A Security Constrained Unit Commitment (SCUC) model for a typical power system was used, which includes thermal and renewable generation units and EES devices in the form of batteries. In the proposed model, the hourly operation strategy is determined by adopting a predetermined level of SR. In order to optimize SR requirements, the Independent System Operator (ISO) runs the SCUC problem and determines the minimum SR that should be provided by generation units and EES devices. The simulation results illustrate that by optimizing the operation of batteries, the ISO can effectively reduce the required capacity of thermal units. Therefore, optimal SR allocation under RES uncertainty is determined in this study.

This paper presents a self-scheduling model for home energy management systems (HEMS) in which a novel formulation of a linear discomfort index (DI) is proposed, incorporating the preferences of end-users in the daily operation of home appliances. The HEMS self-scheduling problem is modelled as a mixed-integer linear programming (MILP) multi-objective problem, aimed at minimizing the energy bill and DI. In this framework, the proposed DI determines the optimal time slots for the operation of home appliances while minimizing end-users’ bills. The resulting multi-objective optimization problem has then been solved by using the epsilon-constraint technique and the VIKOR decision maker has been employed to select the most desired Pareto solution. The proposed model is tested considering tariffs in the presence of various price-based demand response programs (DRP), namely time-of-use (TOU) and real-time pricing (RTP). In addition, different scenarios considering the presence of electrical energy storage (EES) are investigated to study their impact on the optimal operation of HEMS. The simulation results show that the self-scheduling approach proposed in this paper yields significant reductions in the electricity bills for different electricity tariffs. Post-print / Final draft

A new optimization framework based on MILP model is introduced in the paper for the problem of stochastic self-scheduling of hydrothermal units known as HTSS Problem implemented in a joint energy and reserve electricity market with day-ahead mechanism. The proposed MILP framework includes some practical constraints such as the cost due to valve-loading effect, the limit due to DRR and also multi-POZs, which have been less investigated in electricity market models. For the sake of more accuracy, for hydro generating units’ model, multi performance curves are also used. The problem proposed in this paper is formulated using a model on the basis of a stochastic optimization technique while the objective function is maximizing the expected profit utilizing MILP technique. The suggested stochastic self-scheduling model employs the price forecast error in order to take into account the uncertainty due to price. Besides, LMCS is combined with roulette wheel mechanism so that the scenarios corresponding to the non-spinning reserve price and spinning reserve price as well as the energy price at each hour of the scheduling are generated. Finally, the IEEE 118-bus power system is used to indicate the performance and the efficiency of the suggested technique.

Electric vehicles, including both PEVs and PHEVs have been recently interested to a large extent in global markets due to their capabilities. These plug-in vehicles are able to absorb/inject power from/to the electric grid that turns them into an interesting solution for the power systems. However, large numbers of such plug-in vehicles can be a threaten to power systems. In this regard, it seems necessary to investigate the problems caused by the uncertain driving nature of such electric vehicles. On the other hand, the opportunities provided by the presence of a large fleet of plug-in vehicles as mobile storage/load should be discussed. For this end, this paper reviews the challanges and the problems caused by charging/discharging of PHEV/PEVs in large numbers and investigates their capabilities as a solution to integrate the RESs and demand response programs in power systems.

Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system’s operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.