This research compares the frequency and duration of outages within distribution networks for main Nordic land countries (Sweden, Denmark, Finland, and Norway). In addition, this paper focuses on planned and unplanned outages for low- and medium-voltage networks; the consequences of outages for distribution networks and companies; and the level of discomfort experienced by consumers during both planned and unplanned outages. This study highlights the countries with the highest incidence of outages by collecting data from their official reports, compares the frequency and duration of unplanned outages, focuses on SAIFI (System Average Interruption Frequency Index), SAIDI (System Average Interruption Duration Index), and CAIDI (Customer Average Interruption Duration Index)-based outage indices, and examines their outage trends. 

The coupling with natural gas networks creates an excellent opportunity for renewable-rich power systems to facilitate the utilisation of renewable energy resources; on the other hand, with the increase in employment of fuel cell vehicles (FCVs), the number of hydrogen refuelling stations (HRSs) is increasing. The integration of HRSs in electric distribution systems may impact the operation of electric distribution systems. Through vehicle-to-infrastructure (V2I) technology, vehicles may exchange information with HRSs and know hydrogen prices. The operation of the coupled power and natural gas networks with integration of green HRSs, considering the model of FCVs has not been investigated in the literature, so, it has been set as the goal of this paper. To avoid the challenges of nonlinear gas flow model, they are linearized through piecewise linearisation. The case study is a 33-bus electric distribution system, coupled with a 14-node gas distribution network; each HRS includes a battery, a hydrogen tank, an electrolyzer, a PV and a wind turbine. MILP models have been used for FCVs, HRSs and power-gas networks. CPLEX solver is used for solving the developed MILP models. The results show that the total cost of the coupled electricity-gas network is $760.37 and the refuelling cost of each FCV is $17.30. The results indicate that both electric and hydrogen storage systems enhance the flexibility of HRSs, enabling efficient utilisation of PV modules and wind turbines, so, only in few hours, HRSs need to purchase electricity from electric distribution system; that is why each HRS makes a considerable profit of $519 per day. The achieved results also indicate that the pressure of all gas nodes and gas flow of all pipelines fall within their prespecified range.

The key role of hydrogen stations (HSs) is to produce hydrogen and deliver it to hydrogen demands. Offering too high prices by HSs decreases their demand and profit; while offering too low prices decreases their revenue and thereby their profit. In literature, optimal pricing of hydrogen has not been done for HSs. In this research, a risk-averse two-stage stochastic mixed-integer linear model is proposed for optimal pricing of hydrogen for hydrogen consumers in hydrogen stations including hydrogen storage systems. Hydrogen storage capability enables HS operator to purchase more electricity at times with cheaper electricity and offer lower prices for consumers. HS operator sets the prices for demands and procures the required electricity in a way that both its expected profit and risk metric are optimised. The studied HS includes an electrolyzer, a hydrogen storage tank, a transformer and a rectifier, while it is able to procure electricity through a day-ahead electricity market, a photovoltaic (PV) unit and a fuel cell (FC). The pricing is done for residential, industrial and transportation-based demands with different price-quota curves. Hydrogen pricing is done for flat and real-time pricing (RTP) tariffs and the effect of pricing type on HS profit, risk and prices is assessed. The effect of price-quota curves, PV and FC on HS profit, risk and prices are investigated. The results approve the efficiency of the proposed model for hydrogen pricing in HSs. The significant impact of hydrogen storage system on the developed model is verified.

In the power system, economic power dispatch is a popular and fundamental optimization problem. In its classical form, this problem only considers thermal generators and does not take into account network security constraints. However, other forms of the problem, such as economic emission dispatch (EED), are becoming increasingly important due to the emphasis on minimizing emissions for environmental purposes. The integration of renewable sources, such as solar, wind, and hydro units, is an important aspect of EED, but it can be challenging due to the stochastic nature of these sources. In this study, a multi-objective algorithm is developed to address the problem of economic emission power dispatch with the inclusion of these renewable sources. To account for the intermittent behavior of solar, wind, and hydro power, the algorithm uses Lognormal, Weibull, and Gumbel distributions, respectively. The algorithm also considers voltage limitations, transmission line capacities, prohibited areas of operation for thermal generator plants, and system restrictions. The multi-objective real coded non-dominated sorting genetic algorithm II (R-NSGA-II) is applied to the problem and includes a procedure for handling system restrictions to meet system limitations. Results are extracted using fuzzy decision-making and are analyzed and discussed. The proposed method is compared to other newer techniques from another study to demonstrate its robustness. The results show that the proposed method despite being older is cost-significant while maintaining the same or lower emissions. These results were observed consistently and did not happen by chance, detailed explanation of why and how is discussed.

This study formulated a bi-level mixed integer non-linear optimization planning and operation model for the optimal configuration (location, capacity, and power ratings) of energy storage systems (ESSs) in power transmission networks. The model was formulated with consideration for independent and correlated wind farms. The single objective function in the inner layer of the bi-level model includes the difference between the total daily expected operational cost of conventional generators and the energy arbitrage benefits derived when considering the operational strategies of ESSs. The outer layer is a multi-objective function composed of three objective functions to be minimized. The objective functions encompass the total daily expected planning and operational cost, total daily expected emission, and the maximum expected voltage deviation. Wind power uncertainties in independent and correlated wind farms were also examined. Multivariate model-based Clayton copulas, which represent joint power distribution amongst correlated wind farms, were discretized using a developed five-point estimation method based on the discretization. A hybrid non-dominating sorted genetic algorithm and multi-objective particle swarm optimization were used to minimize the outer layer objective function, whilst fast Tabu search that considers the probabilistic load flow represented by wind power uncertainties and the operational strategies of ESSs was adopted to minimize the inner layer objective function. An IEEE 57-bus system was subjected to a case study using the proposed two-stage model. The simulation results confirmed the advantage of considering the benefits of a peak shaving operational strategy from economic, technical, and environmental points of view.

Wind power integration with high penetration in a power system is indispensable. However, wind power integration, especially with high level, raises the power system instability problems due to its natural variability and unpredictability, which increases system uncertainties. Thus, uncertainties and correlations amongst wind farms should be considered in a power system operation and planning. One of the best solutions for facilitating the wind power integration is the installation of an energy storage system (ESS). However, the location and sizing of ESSs should be optimally planned to achieve maximum benefits such as minimising total cost, time shifting, reliability and power quality enhancement, minimising power loss, improving the power factor and providing environmental support. In this paper, a new probabilistic discretising method is derived and developed to discretise the continuous joint power distribution of correlated wind farms. Combining the new probabilistic discretising method with a multi-objective hybrid particle swarm optimisation (MOPSO) and non-dominated sorting genetic algorithm (NSGAII), a new hybrid probabilistic optimisation algorithm is proposed. The proposed hybrid algorithm aims to search for the best location and size of energy storage system (ESSs) and considers the power uncertainties of multi-correlated wind farms. The objective functions to be minimised include a system's total expected cost restricted by investment budget, total expected voltage deviation and total expected carbon emission. IEEE 30-bus and IEEE 57-bus systems are adopted to perform the case studies using the proposed hybrid probabilistic optimisation algorithm. The simulation results demonstrate the effectiveness of the proposed hybrid method in solving the optimal allocation problem of ESSs and considering the uncertainties of wind farms’ output power and the correlation amongst them.

The growth of demand, the need for economic efficiency and optimal utilisation of electric power networks and the high cost of construction of new power networks result in inevitable challenges, such as overloading and excessive power transfer along transmission lines, high losses, voltage instability, low power quality, reliability problems and voltage profile problems. To manage the power transmission system, the recently developed Flexible AC Transmission System (FACTS) can be used for electric transmission networks since it plays an important role in enhancing the static and dynamic performance of power systems. However, location, type and capacity of FACTS devices should be optimised to maximise the resulting benefits. In this paper, different types of FACTS devices are discussed along with their modelling and functions. In addition, the proposed and the compared techniques and approaches in the existing research works, such as analytic approaches, arithmetic programming methods, meta-heuristic optimisation techniques and hybrid methods, are discussed. Analytic approaches have insufficient computation accuracy in determining optimal allocation of FACTS devices and arithmetic programming approaches are often inefficient in managing constrained optimisation problems. However, meta-heuristic approaches are stochastic, population-based optimisation algorithms that are highly efficient in dealing with a multimodal, highly constrained, multi-objective and discrete system. Meta-heuristic techniques are the most commonly used methodologies to determine the optimal allocation of FACTS devices. Furthermore, the utilisation of analytic methods or classical optimisation approaches with meta-heuristic optimisation techniques plays an important role in reducing the search space of the proposed meta-heuristic optimisation technique. In the present paper, an overall review of 50 recent research work studies, including proposed and compared approaches and techniques, objective functions, approaches, the utilised FACTS devices, constraints, contingency conditions and all the analysed and simulated parameters, is provided and discussed in details. In addition, a more weighted discussion of the proposed methods based on meta-heuristic optimisation techniques is provided.

Despite the increasing utilization of renewable energy resources, such as solar and wind energy, most residential buildings still rely on conventional energy supply by public utility services. Such utility services often use time-of-use energy pricing, which compels residential consumers to reduce their energy usage. This paper presents a wireless home energy management (HEM) system that enables the automatic control of home appliances to reduce energy consumption to assist such energy users. The system consists of multiple smart sockets that measure the energy that is consumed by the connected appliances and are capable of implementing on/off commands. The system includes other support components for supplying data to a central controller, which utilizes a rule-based HEM algorithm. The control rules were designed, such that the lifestyle of the user would be preserved while the energy consumption and daily energy cost were reduced. The experimental results showed that the central controller could effectively receive data and control multiple devices. The system was also found to afford significant reductions of 23.5 kWh and $2.898 in the total daily energy consumption and bill of the considered household setup, respectively. The proposed HEM system promises to be particularly useful for households with a high daily energy consumption.

Proton exchange membrane fuel cell is an emerging renewable energy resource for transportation and power generation. Similar to other renewable resources, the performance of proton exchange membrane fuel cell is affected by ambient conditions. However, procedures for analyzing the influences of such conditions on the performance of proton exchange membrane fuel cells are expensive and time-consuming. Moreover, the commonly used models have been developed on the basis of standard ambient conditions. Thus, these models are difficult to utilize under adverse ambient conditions. This study was performed to develop suitable proton exchange membrane fuel cell models that could reflect the effects of ambient conditions on the output voltage and current of the models. The first proposed model used the advantages of electrical and thermal relationships of a complex semiempirical model of a proton exchange membrane fuel cell. A simplified proton exchange membrane fuel cell model that used passive electrical components was then developed by central composite surface design. Both proposed models were simulated using various ambient temperatures, pressures, and load resistances by considering that the applied hydrogen pressure is known. Results showed that the output voltage of proton exchange membrane fuel cell decreased when ambient temperature increased and pressure decreased. This variation was dominant when the load resistance was reduced. Computation using the simplified model was remarkably faster than that using the first model. The proposed model can be beneficial, especially for aircraft applications and unusual ambient conditions.

Solar energy is a source of free, clean energy which avoids the destructive effects on the environment that have long been caused by power generation. Solar energy technology rivals fossil fuels, and its development has increased recently. Photovoltaic (PV) solar farms can only produce active power during the day, while at night, they are completely idle. At the same time, though, active power should be supported by reactive power. Reactive power compensation in power systems improves power quality and stability. The use during the night of a PV solar farm inverter as a static synchronous compensator (or PV-STATCOM device) has recently been proposed which can improve system performance and increase the utility of a PV solar farm. In this paper, a method for optimal PV-STATCOM placement and sizing is proposed using empirical data. Considering the objectives of power loss and cost minimization as well as voltage improvement, two sub-problems of placement and sizing, respectively, are solved by a power loss index and adaptive particle swarm optimization (APSO). Test results show that APSO not only performs better in finding optimal solutions but also converges faster compared with bee colony optimization (BCO) and lightening search algorithm (LSA). Installation of a PV solar farm, STATCOM, and PV-STATCOM in a system are each evaluated in terms of efficiency and cost.