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The Under Frequency Load Shedding (UFLS) is the last automated action to restore power system frequency stability when a severe contingency causes a significant drop in the frequency of interconnection or islanded areas. In this paper, a novel probabilistic algorithm that considers the wind turbine frequency response is proposed to design and optimize the UFLS scheme. The uncertainties of power systems such as wind speed, hourly load, inertia time constant, and generation losses are taken into account. The Monte-Carlo Simulation (MCS) method is used to model the power system uncertainties. Additionally, a novel method is developed to calculate the power system frequency response and accelerate the multistage UFLS scheme optimization processes. In this method, a time-independent formulation for power system frequency response is utilized to reduce the computational complexity. More than Twenty different UFLS plans are designed using the proposed probabilistic algorithm. Besides, some time-domain simulations are carried on the modified IEEE 39-bus test system to investigate the performance of the designed UFLS schemes. Results indicate that designed UFLS schemes provide stable frequency response and improve the power system frequency stability.

This paper analyzes the oligopolistic equilibrium of multiple price-maker agents in performance-based regulation (PBR) markets. In these markets, there are price-maker agents representing some frequency regulation (FR) providers and a number of independent price-taker FR providers. An equilibrium problem with equilibrium constraints (EPEC) model is employed in this paper to study the equilibria of a PBR market in the presence of price-maker agents and pricetaker FR providers. Due to the incorporation of the FR providers' dynamics, the proposed model is reformulated as a mixed-integer linear programming (MILP) problem over innovative mathematical techniques. An optimal equilibrium point is also selected for the market, where none of the agents is unique deviator and the dynamic performance of power system is improved simultaneously. The effectiveness of proposed optimal equilibrium point is evaluated in the numerical results section by comparing the outputs with conventional optimal dispatches of the FR providers.

An integrated view of energy markets has led to the introduction of new market participants named energy retailers (ERs). Competition among ERs in an integrated market greatly improves market performance in areas such as sustainability, supply security, and environmental awareness. ER performance is evaluated in an integrated market with emission penalty reduction in the objective function. Bi‐level programming is used to model an ER game, and the equilibria of different configurations are compared. In the game's first level, ERs offer various energy carrier selling prices to optimise profits. In the second level, consumers respond by switching from the retail market to minimise their energy bills. This complex problem is simplified utilising a discretisation method based on Nash theory. The configuration scenarios include different structures, components, and ER–user transactions. The effectiveness of the proposed configuration scenarios is validated by their implementation in an illustrative case study. Energy retailers purchase energy from various sources including bilateral contracts with energy suppliers, wholesale market participation, and self‐generation. Electricity retailers explicitly represent the difficulties of energy procurement and bidding strategies. Simulation results for the proposed energy system model indicate better performance than that of the base case and a noticeable increment in retailer profit.

In this letter, an incentive compatible contract is designed for purchasing energy and ancillary service (AS) simultaneously from strategic frequency regulation service providers (FRSPs) by considering information asymmetries between independent system operator and FRSPs. Here, AS is confined to frequency reserve (FR) for arresting nadir frequency following possible contingencies of the electrical grid. An FRSP has a multi-dimensional private information vector, which determines its operational cost and limitations. Moreover, there is a gaming opportunity for an FRSP that is arisen between energy and AS payments manifesting by manipulation of its bids. Thus, in this letter, a multi-object contract is designed by using the revelation principle to optimally allocate energy and FR among FRSPs.

Multicarrier energy systems have provided the opportunity for users who are experiencing only must-run loads to participate in demand response programs by using self-generation units. In this paper, a bilevel game between so-called energy retailers and consumers with firm loads (as subsystems) is formulated in a multicarrier energy system. In the first level of this game, energy retailers offer selling prices for different energy carriers to maximize their profit. Consequently, consumers react to the offered prices by managing their purchasing power from the retail market to minimize their energy bill in the second level. The proposed approach leads to a mixed integer nonlinear programming problem which is sophisticated to solve. To simplify this complex problem, a solution method based on discretization technique is introduced to approach to an epsilon Nash point. The better performance of the proposed energy system model is shown in comparison with the base case and its effectiveness is validated experimentally over a practical benchmark system. The simulation results show a noticeable reduction in the energy production cost and users’ energy bill. Moreover, energy retailers gain more profit in the proposed approach.

Besides increasing the installation of distributed generation plants, investigation on multi-carrier energy systems leads recent studies to focus on several aspects of Smart Energy Hub (S. E. Hub) systems. An S. E. Hub incorporates several modules which calculation of optimal size and operation of each have already attracted a great deal of research. Uncertainty in the modeling of these modules is an imperative factor that was not paid attention in S. E. Hub models properly. To build up a more precise framework for S. E. Hubs, here we present a stochastic model for real time electricity and natural-gas prices and electricity demands. In this paper, an S. E. Hub operates in order to minimize a weighted sum function consisting energy bill and penalty for emissions. To have more precise model, we use conditional value at risk (CVaR) technique to control the operational risk of an S. E. Hub when electricity and natural gas are converted to electrical, heating, and cooling energy in its output ports. The proposed optimization method is validated by simulating it on a real office building in Tehran, Iran.

This paper introduces a day-ahead clearing model for the simultaneous energy and ancillary services market in high penetration of renewable energy sources (RESs) considering fast-start generators’ behaviors as non-spinning reserve providers. For managing the uncertainties of RESs, the system operator takes the advantages of the stochastic model of security-constrained unit commitment (SCUC) problem considering plausible scenarios. The non-convexities due to the startup costs, minimum power outputs, and commitment variables make the stochastic SCUC problem and market-clearing non-convex. Meanwhile, with traditional market-clearing methods for energy and ancillary services based on marginal costs or dual variables, the net profit of some generators may become negative. In this situation, more financial losses will be imposed on fast-start generators since they are marginal/intermittent units for managing the uncertainties of RESs. Using a duality gap minimization and profit insurance, this paper presents a pricing model to solve these issues. The proposed market-clearing model leads to uniform prices for ancillary services and nodal prices for energy guaranteeing the non-negative net profit of all generators. The results of the 3-buses system and IEEE 118-buses system are analyzed to illustrate the applications of the proposed pricing approach.

Today, energy crises attracted many researchers’ attention to renewable energy technologies especially photovoltaic (PV) systems. The main challenge of PV systems is unpredictable nature of solar power generation. To overcome this challenge, a storage system is integrated which reduces demand reliance on electricity grid and uses excess energy that solar panels produce. As investment cost of the storage system is considerable, finding an optimal technology, size, and configuration are crucial. In this paper, the optimal battery system is excluded from existing PV plant installing in a commercial building located in Mashhad/Iran. Here, the sizing procedure is based on a financial evaluation which considers the damage costs due to outages that the commercial building is experiencing during the PV system life spam. To compare results, the battery size is also determined by stochastic methods, e.g. Monte-Carlo simulation method. The simulation results confirm the advantages of the proposed approach compare with classic ones.

Integrated view of energy markets leads to the introduction of new market participants named Energy Retailer (E.R). The competition between E.Rs in an integrated market, significantly improves the market performance in different aspects such as sustainability, security of supply and environmental concerns. In this paper, the performance of the E.R has been evaluated in an integrated market considering the emission penalty reduction in the objective function. The performance of the E.R has been evaluated in different market structures comparing the share of the E.R in energy trading and the carbon emissions in multi-carrier energy system.

With the fast proliferation of intermittent renewable energy sources and fluctuations in load demand, power systems need to withstand an increasing number of disturbances that may affect the system frequency. In this paper, the highest acceptable penetration level of wind generation is formulated and calculated with and without considering operating reserve constraints as a function of equivalent governor response and system inertia. Afterward, the feasibility of wind turbine participation in the power system frequency regulation task is investigated and the minimum required wind turbine frequency response is calculated as a function of penetration level. This study uses a discretized model of dynamic system frequency response to estimate the minimum frequency after contingencies. To investigate the proposed methods, some sensitivity analyses are carried out on IEEE 39-bus test system. Simulations results illustrate a more secure and reliable operation of the power system when the wind turbines participate in the frequency regulation task.

To decrease dependency on fossil fuels, almost all countries plan to invest more in renewable energy sources (RESs), e.g., wind and solar power plants. RESs provide substantial benefits for our climate and economy. However, there are still some technical and financial challenges to generate large amount of power through RESs. Increasing RESs capacities in a power system escalates the risk of instability. In this paper, RESs are deployed in the framework of virtual power plants (VPPs). The VPPs, as a new concept in power systems and electricity markets, have a substantial impact on increasing efficiency, engagement, and synergy effects of RESs in power systems. Optimal amounts of power generation of different generating units in a sample VPP are calculated and financial indicators are studied in this paper. To achieve more reasonable results with respect to stochastic nature of wind and solar generations, we apply Information Gap Decision Theory (IGDT) method to evaluate the system uncertainty. The results are achieved by GAMS software. Moreover, the impacts of different sources of uncertainties are compared with each other.

United Nation aims to double the global rate of improvement in energy efficiency as one of the sustainable development goals. It means researchers should focus on energy systems to enhance their overall efficiency. One of the effective solution to move from suboptimal energy systems to optimal ones is analyzing energy system in Energy Hub (EH) framework. In EH framework, interactions between different energy carriers are considered in supplying the required loads. The couplings and selecting proper combinations of inputs energy carriers lead to more optimized and intelligent consumption. The appropriate combination is found by solving an optimization problem at each time step. Utilizing intelligent technologies such as Advanced Metering Infrastructures (AMIs) inevitably facilitate the decision making processes. This paper modifies the classic Energy Hub model to present an upgraded model in the smart environment entitling “Smart Energy Hub” and optimizes the operation of a residential customer equipped with combined heat and power (CHP), auxiliary boiler, electricity storage and heating storage in this framework. Supporting real time, two-way communication between utility companies and smart energy hubs, and allowing AMIs at both ends to manage power consumption necessitates large-scale real-time computing capabilities to handle the communication and the storage of huge transferable data. To address this concern and reduce the amount of calculations, Reinforcement Learning (RL) method is employed to find a near optimal solution, which does not need massive computations. Finally, communications to large numbers of endpoints in a secure, scalable, and highly-available environment, in this paper, we propose a cloud computing (CC) architecture. Simulation results show that by applying RL technique in smart energy hub framework for a residential customer, efficiency of the energy system is increased substantially and leads to decrease energy bills and electricity peak load.

Today, as a consequence of the growing installation of efficient technologies, e.g. micro-combined heat and power (micro-CHP), the integration of traditionally separated electricity and natural gas networks has been attracting attentions from researchers in both academia and industry. To model the interaction among electricity and natural gas networks in distribution systems, this paper models a residential customer in a multi-energy system (MES). In this paper, we propose a fully automated energy management system (EMS) based on a reinforcement learning (RL) algorithm to motivate residential customers for participating in demand side management (DSM) programs and reducing the peak load in both electricity and natural gas networks. This proposed EMS estimates the residential customers’ satisfaction function, energy prices, and efficiencies of appliances based on the residential customers’ historical actions. Simulations are performed for the sample model and results depict how much of each energy, i.e. electricity and natural gas, the residential customer should consume and how much of natural gas should be converted in order to meet electricity and heating loads. It is also shown that the proposed RL algorithm reduces residential customer energy bill and electrical peak load up to 20% and 24%, respectively.

The development of technologies such as micro turbines and gas furnaces has been the major driving force towards integration of electricity and natural gas networks in the EH (energy hubs). Besides, the existing power grids are getting smarter by implementing the new generation of information technologies and communication systems. In smart grid, the electricity suppliers can modify the customers' electrical load consumption by employing appropriate scheduling schemes such as DR (demand response) programs. In this paper, we consider the S. E. Hubs (smart energy hubs) framework, in which the customers can use EMS (energy management system) to access to the electricity and natural gas prices data and wisely manage their daily energy consumption. We extend the existing DR programs to the IDR (integrated demand response) programs with the aim of modifying both electricity and natural gas consumption on the customer side. The interaction between S.E. hubs in the IDR program is formulated as a non-cooperative game. The goal of the IDR game is to maximize the natural gas and electricity utility companies' profit and to minimize the customers' consumption cost. It is shown that the proposed game model is an ordinal potential game with unique Nash equilibrium. Simulations are performed on an energy system with 6 S. E. Hubs, one electricity utility, and one natural gas utility companies. The results confirm that the IDR program can benefit both the customer side, by reducing the electricity and gas consumption cost, and the supplier side, by reducing the peak load demand in the electricity and natural gas load profiles.

The presence of energy hubs in the future vision of energy networks creates an opportunity for electrical engineers to move toward more efficient energy systems. At the same time, it is envisioned that smart grid can cover the natural gas network in the near future. This paper modifies the classic Energy Hub model to present an upgraded model in the smart environment entitling “Smart Energy Hub”. Supporting real time, two-way communication between utility companies and smart energy hubs, and allowing intelligent infrastructures at both ends to manage power consumption necessitates large-scale real-time computing capabilities to handle the communication and the storage of huge transferable data. To manage communications to large numbers of endpoints in a secure, scalable and highly-available environment, in this paper we provide a cloud computing framework for a group of smart energy hubs. Then, we use game theory to model the demand side management among the smart energy hubs. Simulation results confirm that at the Nash equilibrium, peak to average ratio of the total electricity demand reduces significantly and at the same time the hubs will pay less considerably for their energy bill.

The concept of smart grid SG has been introduced to improve the operation of the power systems. In modern structures of power systems, different reasons prompt researchers to suggest integrated analysis of multi-carrier energy systems. Considering synergy effects of the couplings between different energy carriers and utilising intelligent technologies for monitoring and controlling of energy flow may change energy system management in the future. In this paper, we propose a new solution which is entitled ‘smart energy hub’ SEH that models a multi-carrier energy system in a SG. SEH solutions allow homeowners to manage their energy consumption to reduce their electricity and gas bill. We present this concept for a residential customer by an ‘energy management system’ which uses reinforcement learning algorithm and Monte Carlo estimation method for finding a near optimal solution. The simulation results show that by using this concept and then applying the algorithm for a residential customer, running costs are reduced up to 40% while keeping the household owner's desired comfort levels.

Implementation of various incentive-based and time-based load management strategies has great potential to decrease peak load growth and customer electricity bill cost. In recent years, developments in Plug in Hybrid Electric Vehicles (PHEVs) have provided various environmental and economic advantages. However, high penetration of electric vehicles in to the grid may cause high peak loads at different times of the days. Using advanced metering and automatic chargers makes it possible to optimize the charging cost, and release generation capacities to provide sustainable electricity supply. Using an appropriate encouraging program is a simple way for vehicle owners to manage their energy consumption and shift the time of charging to proper time of the day; and therefore, to reduce their electricity bill. With these objectives, this paper proposes a new practical PHEVs’ charging scheduling programs aiming at optimizing customers charging cost by considering the generation capacity limitation and dynamic electricity price in different time slots of a day. Using a stochastic model for start time of charging and the duration of it in the proposed optimization algorithm, make this method a practical tool for modeling the vehicle owners’ charging behavior with the purpose of peak load shaving.

Cost of annual energy loss occurs in distribution networks is an important issue in distribution systems planning, design and operation & discos are pleased to be informed about financial worth of loss extract from their profit to evaluate economic feasibility of improvement projects. there are several dominating factors involved in distribution loss cost evaluation, but most of the times price of loss occurs in distribution systems is calculated regardless of location, consumption pattern and etc, but recent changes in Iran distribution power system including removing energy subsidies, deregulation, increase in distributed generation units and trend toward loss reduction had been resulted in much more importance to have actual price of distribution network losses. In this paper, Distributed generation units penetration level influence on distribution networks worth of loss is investigated, power delivered to distribution systems from these resources is directly connected to loads and no transmission cost is involved, in the proposed formulation this difference between centralized and decentralized generation is covered. It is shown that distribution worth of loss follow a U-shape trajectory by increase in distributed generation units' penetration level.

The interest in distributed generation has been increasing in recent years, especially due to technical devel- opment on generation systems that meet environmental and energy policy concerns. One of the most impor- tant distributed energy technologies is Combined Cooling, Heat and Power (CCHP) systems. CCHP is a small and self-contained electric, heating and cooling generation plant that can provide power for households, commercial or industrial facilities. It can reduce power loss and enhance service reliability in distribution systems. The proposed method in this paper determines the optimal size and operation of CCHP, auxiliary boiler and also heat storage unit as elements of an energy hub, for users by an integrated view of electricity and natural gas network. Authors apply cost and benefit analysis in the optimization. To confirm the proposed method, the optimum sizes of these elements are determined for a hotel in Tehran as a case study.

In recent years, there have been significant developments of applications related to “Energy Hub” and “Smart Grid” concepts. Synergy effect of the coupling between electricity and natural gas infrastructures, and approaching energy systems to the smart grid environment leads us to introduce a new solution. This solution is entitled “Smart Energy Hub” (S. E. Hub). Simply stated, the S. E. Hub models a multicarrier energy system in a smart grid environment. The evolution of the smart grid heavily relies on the utilization and the integration of modern information technologies. Therefore, we suggest that the information technology industry should be involved to facilitate the information management in the smart grid. More specifically, we explore how cloud computing, a next-generation computing paradigm, can serve the information management in the smart grid. In this article, we propose demand side management (DSM) game among a group of S. E. Hubs in the cloud computing framework. The simulation results confirm that the proposed approach can reduce the “peak to average ratio” of the total electricity demand as well as individual energy cost.

The interests in “Energy Hub” (EH) and “Smart Grid” (SG) concepts have been increasing, in recent years. The synergy effect of the coupling between electricity and natural gas grids and utilizing intelligent technologies for communicating, may change energy management in the future. A new solution entitling “Smart Energy Hub” (S. E. Hub) that models a multi-carrier energy system in a SG environment studied in this paper. Moreover, the optimal size of CHP, auxiliary boiler, absorption chiller, and also transformer unit as main elements of a S. E. Hub is determined. Authors proposed a comprehensive cost and benefit analysis to optimize these elements and apply Reinforcement Learning (RL) algorithm for solving the optimization problem. To confirm the proposed method, a residential customer has been investigated as an S. E. Hub in a dynamic electricity pricing market.

Plug-in Hybrid Electric Vehicles (PHEV) have the potential to limit emissions and reduce the cost of transportation. Although wide scale adoption of plug-in vehicles is still a few years away, politicians, electric utilities, and auto companies are eagerly awaiting the opportunities that may arise from reduction of emissions and gasoline and oil consumption, new services and increased revenues, and new markets that would create new jobs. For consumers, plug-in vehicles will significantly lower operational costs when compared with traditional gasoline cars. The savings are potentially high, in average, as electricity cost mile work out to about one forth of gasoline. But it is noticeable to consider PHEV effects on distribution systems. Plug-in vehicles will represent a substantial new load on the existing distribution networks and will significantly alter the load pattern of consumption. In this paper, influence of intelligent distribution network on plug-in hybrid electric vehicle penetration level is investigated. It is shown that cost saving from society point of view follows an ∩-shape trajectory by increase in plug-in hybrid electric vehicles' penetration level in a definite smart grid level. It is demonstrated by increasing smart grid penetration level, optimum number of PHEV increase significantly.

Today, the interest on distributed generation has been increasing, especially due to technical development on generation systems that meet environmental and energy policy concerns. One of the most important distributed energy technologies is Combined Heat and Power (CHP). CHP is small and self-contained electric generation plan that can provide power for household applications, commercial or industrial facilities and hence its applications are overtly increasing. It can reduce power loss and enhance service reliability in distribution systems. One of the influential factors for the users is the purchasing cost of CHP which is largely dependent on its type, capacity and efficiency. Therefore among all exciting of the commercial CHP technologies certain economic choices are to be taken into account. Cost-Benefit analysis is one of the famous approaches aiming at maximization of benefit-to-cost ratio. The proposed method in this paper determine the optimized size of CHP for users by considering an integrated view of electricity and natural gas network based on the novel concept of energy hubs. The benefits of CHP placement equipped with a heat storage device described in this paper include power cost saving, power loss reduction, reliability enhancement and environmental considerations. At last to confirm the proposed method the best size of CHP is determined for a hotel as a case study.

Today, the interest on distributed generation has been increasing, especially due to technical development on generation systems that meet environmental and energy policy concerns. One of the most important distributed energy technologies is Combined Heat and Power (CHP). CHP is small and self-contained electric generation plan that can provide power for household applications, commercial or industrial facilities and hence its applications are overtly increasing. It can reduce power loss and enhance service reliability in distribution systems. One of the influential factors for the users is the purchasing cost of CHP which is largely dependent on its type, capacity and efficiency. Therefore among all exciting of the commercial CHP technologies certain economic choices are to be taken into account. Cost-Benefit analysis is one of the famous approaches aiming at maximization of benefit-to-cost ratio.

Today, the interest on distributed generation has been increasing, especially due to technical development on generation systems that meet environmental and energy policy concerns. One of the most important distributed energy technologies is Combined Heat and Power (CHP). CHP is small and self-contained electric generation plan that can provide power for household applications, commercial or industrial facilities and hence its applications are overtly increasing. It can reduce power loss and enhance service reliability in distribution systems. One of the influential factors for the users is the purchasing cost of CHP which is largely dependent on its type, capacity and efficiency. Therefore among all exciting of the commercial CHP technologies certain economic choices are to be taken into account. Cost-Benefit analysis is one of the famous approaches aiming at maximization of benefit-to-cost ratio. The proposed method in this paper determine the optimized size of CHP for users by considering an integrated view of electricity and natural gas network based on the novel concept of energy hubs.

Random pulse width modulation (RPWM) technique has been employed to generate the control signals of Vienna-type rectifier. This technique is compared to two conventional techniques (Hysteresis-band and ramp comparison with fix frequency carrier signal). Simulation results are illustrated for three approaches. The results verify significantly reduction of high frequency harmonics magnitude; and consequently overcome electromagnetic interference (EMI) problem in this active type of rectifier.

The proliferation of technologies such as combine heat and power systems has accelerated the integration of energy resources in energy hubs. Besides, the advances in smart grid technologies motivate the electricity utility companies toward developing demand response (DR) programs to influence the electricity usage behavior of the customers. In this paper, we modify the conventional DR programs in smart grid to develop an integrated DR (IDR) program for multiple energy carriers fed into an energy hub in smart grid, namely a smart energy (S. E.) hub. In our model, the IDR program is formulated for the electricity and natural gas networks. The interaction among the S. E. hubs is modeled as an ordinal potential game with unique Nash equilibrium. Besides, a distributed algorithm is developed to determine the equilibrium. Simulation results show that in addition to load shifting, the customers in the S. E. hubs can participate in the IDR program by switching the energy resources (e.g., from the electricity to the natural gas) during the peak hours. Moreover, the IDR program can increase the S. E. hubs’ daily payoff and the utility companies’ daily profit.