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DC Microgrid (MG) with DC distribution system is an attractive technology over the last decade due to its inherent compatibility with renewable energy sources (RESs), DC loads, and storage devices. The worldwide growing concern on global warming and reduction of fossil fuel has raised the need for clean and eco-friendly RESs for electricity generation through the MG concept. DC MG can be linked to the main electrical grid or can act in isolation. Hence it is an effective system for isolated and rural areas, and it is accessible from small buildings up to huge plants. This paper reports an overview of the latest possible techniques and challenges in standardization, control approaches, protection issues associated with DC MG and DC distribution system. This paper also highlights the importance of DC MG for DC loads.

A universal analytical expression is obtained for the power of hydraulic turbines of small hydropower plants such as Francis, Pelton and Kaplan with fixed blades as a function of water flow. This expression allows us to predict the generation of active power of each of the turbines, depending on the adjusted water flow rate, which varies in the seasonal, monthly and daily periods of the year. It also allows you to solve the inverse problem, when it is necessary, depending on the load schedule dictated by the electrical system to which these small hydroelectric power stations are connected, to regulate the amount of water flow by changing the opening angles of the guiding devices of the hydraulic turbines. Studies on the proposed mathematical model containing a synchronous generator with electromagnetic excitation coupled to a Francis hydraulic turbine with a 60% change in water flow from q=1 (r.u) to q=0.4 (r.u). The value of the required active power of a small hydroelectric power station, i.e. power output of the generator will jam (either by the dispatcher, or by the value of the load schedule), i.e. it is an input value, and as the output value of the controller is the opening angle of the guiding apparatus of the turbine.

The problems of the influence of voltage deviation on the operation of electrical networks and electricity consumers are considered. The influence of the voltage level on the efficiency of the modes of electrical networks, the occurrence of additional energy losses in the network elements and electrical receivers is investigated.

What does the United States need to do to start a renewables revolution and get its electrical grid to 50, 60, or even 90 percent green energy? That’s a question that Greg Nemet, the author of How ...

This paper presents the integration of a solar system into road lighting systems based on a nanogrid concept, the traditional road lighting system improvement employing light-emitting diode technologies and solar energy systems. The power quality of the proposed nanogrid road lighting system is studied and analyzed in different modes, namely, stand-alone and grid-connected, using an experimental test set. Additionally, the road lighting equipment and power quality control regulation for the grid-connected system are studied in accordance with the IEC 61000-3-2 (2018) Class C standard. The economic assessment of the proposed road lighting systems is compared with a conventional solar farm system installation in terms of discounted payback period, net present value, and internal rate of return. The results indicate that the system can effectively charge the batteries with a maximum power point based on the maximum power point tracking function and supply the road lighting system through the power from the batteries. In the grid-connected operation, the electrical energy harvested from the solar system can be sent to the electrical grid with the maximum power point. The integration of a solar system into the road lighting system based on the nanogrid concept offers remarkable economic performance in investments. Furthermore, it provides a greater economic feasibility than investing in a solar farm system, as the former is free of land costs.

Active filters based on multicellular inverters are an efficient, robust, and reliable means of large-scale photovoltaic systems for the next generation of smart grids. This paper presents active filters based on a cascaded multicellular inverter for three-phase PV systems connected to the North Cameroon interconnected grid. The proposed system consists of the boost chopper connected to the grids, via the 7-level inverters located before the multicellular active filters with five switching cells per arm. The contribution of this paper is due to the improved P&O MPPT algorithm for the extraction of the maximum power produced by the PV generators and the appropriate choice of the active filters to reduce the harmonic distortion rate to an acceptable value by the grid regulations for a decentralized generation. After synchronization of the system with the electrical grids, the voltage and current of the grid remain in phase. This means that the power factor is corrected. The results show that the system can reduce the harmonic distortion from 23.06% to 0.42% when the active power of the photovoltaic generators is injected into the electrical grids.

This article offers synchrophasor transient and steady compliance with IEC/IEEE 60,255-118-1:2018 standard. This power system standard is mainly designed for the synchronized phasor measurement and specifies the synchronized voltage and current phasors, frequency and derivative requirements, time tag, and synchronization of all measurements. The Synchrophasor testbed includes three-phase high-precision power and current signal voltage, system time unit, GPS receiver, server PC, electricity standards, PMUCAL and test-based PMU software. Three-Phase Measurement Units (PMUs) from different machines have been tested individually to confirm compliance with the state-of-the-art international standard, which is essentially a dual logo standard. Such measured information is being used to control the power grid by smart communication utility. The test results of synchrophasor have been analyzed and discussed.

1. IntroductionTo understand the meaning of the benefits that smart grids can bring we must firstly establish our understanding of the concept. Therefore, we begin with the purpose for which smart grids are being installed in the first place. As technologies are progressing faster and faster, the national and global markets have a much higher demand for electricity. For this reason a technology development process is currently under way that would allow us to accomplish a higher performance and a more effective usage of electricity in the electrical grid. In this case the most popular upgrade for electrical grid is the installing of smart grids. A smart grid is an electrical grid, which is being modernized and renewed by installing new technologies, which would allow having a two-way communication between the consumer and supplier. Such smart grid installation of current electrical grid can increase the electricity distribution and reliability, but also save money to both consumer and supplier. For smart grids to meet the standards and to be effective, they have to be sustainable. Sustainable smart grids are those, that meet all three dimensions of sustainable development: (1) are able to work economically and are cost saving, (2) are environmentally friendly, connecting renewable energy sources, and (3) allow users and suppliers to have a two-way communication.In order to simplify the information, electrical networks can be defined as a set of hardware and wiring machines, which connect the power source (e.g. power plant) with consumers and their many needs. Power plants transform the initial form of energy, such as chemical energy stored in coal, glowing solar energy, wind, pressure or energy stored in the nucleus of uranium, into electricity, which is no more than a temporary, flexible and portable form of energy. The most important thing is to remember that electricity is not fuel: it is an energy carrier. In the electricity network and in factories or homes, electricity is converted back into a useful form of energy or activity as heat, light, information processing or torque motors (Amin and Stringer, 2008).The current problems of smart grids, mostly related to countries that have a lower standard of living and haven't developed their electrical grid system are as follows:1) Old (most plants are 35 years old or more);2) Contamination (more than half of the electricity is produced from coal burning);3) Inefficiencies (supplied electrical efficiency is only 35%);4) Instability.This means that the countries that have old electrical grids have a high contamination from being unsustainable (e.g. coal burning), have low electricity efficiency, productivity and are technically unstable. We will further explore why they would have a various advantages from installing smart grids into their electrical grid system (Hicks, 2012).In order to meet the electricity transmission and consumption needs, there has been a need to create and use smart grids. An intelligent network is a complex structural unit communicating with each other as a whole. The network key components are: energy production; transmission and distribution networks; active users; operations center; energy exchange; service, energy storage devices. (Galdikas et al., 2014)Smart networks produce and respond to information in order to create a better electrical system. Smart grid technologies include: switches, sensors, software, timers and many other components, which now allow two-way communication throughout the energy system. These technologies can be placed anywhere on your smart network, starting and ending with power transformer substations, as well as electricity supply lines into consumer homes. Using the information technologies, smart grids can help to save energy, to facilitate the integration of renewable energy and make the electricity grid more durable, more resistant and more reliable (Succar and Cavanagh, 2012). …

Minibus taxi public transport is a seemingly chaotic phenomenon in the developing cities of the Global South with unique mobility and operational characteristics. Eventually this ubiquitous fleet of minibus taxis is expected to transition to electric vehicles, which will result in an additional energy burden on Africa's already fragile electrical grids. This paper examines the electrical energy demands of this possible evolution, and presents a generic simulation environment to assess the grid impact and charging opportunities. We used GPS tracking and spatio-temporal data to assess the energy requirements of nine electric minibus taxis as well as the informal and formal stops at which the taxis can recharge. Given the region's abundant sunshine, we modelled a grid-connected solar photovoltaic charging system to determine how effectively PV may be used to offset the additional burden on the electrical grid. The mean energy demand of the taxis was 213kWh/d, resulting in an average efficiency of 0.93kWh/km. The stopping time across taxis, a proxy for charging opportunity, ranged from 7.7 h/d to 10.6 h/d. The energy supplied per surface area of PV to offset the charging load of a taxi while stopping, ranged from 0.38 to 0.90kWh/m2 per day. Our simulator, which is publicly available, and the results will allow traffic planners and grid operators to assess and plan for looming electric vehicle roll-outs.

Electric Vehicles (EV) impact urban networks both when driving (e.g., noise and pollution reduction) and charging. For the electrical grid, the flexibility of EV charging makes it a significant actor in "Demand Response" mechanisms. Therefore, there is a need to design incentive mechanisms to foster customer engagement. A congestion game approach is adopted to evaluate the performance of such electrical transportation system with multiple classes of vehicles: EV and Gasoline Vehicles. Both temporal and energy operating costs are considered. The latter is nonseparable as it depends on the global charging need of all EV, which is scheduled in time by a centralized aggregator in function of nonflexible consumption at charging location. Thus, driving and charging decisions are coupled. An adaptation of Beckmann's method proves the existence of a Wardrop Equilibrium (WE) in the considered nonseparable congestion game; this WE is unique when the charging unit price is an increasing function of the global charging need. A condition on the nonflexible load is given to guarantee the monotonicity of this function. This condition is tested on real consumption data in France and in Texas, USA. Optimal tolls are used to control this electrical transportation system and then computed in order to minimize an environmental cost on a simple network topology., Comment: 11 pages, 8 figures, submitted to IEEE Transactions on Intelligent Transportation Systems

We consider the classical power management problem: There is a system or “device” which has two states—ON and OFF—and one has to develop a control algorithm for changing between these states as to minimize cost (energy or some other hybrid cost) when given a sequence of service requests. We analyze this problem in terms of online competitive analysis to obtain worst-case guarantees. Although an optimal 2-competitive algorithm exists, that algorithm does not result in good performance in many practical situations, especially in case the device is not used frequently. To take the frequency of device usage into account, we construct an algorithm based on the concept of “slackness degree”. Then by relaxing the worst-case competitive ratio of our online algorithm to $$2 + \varepsilon$$ , where $$\varepsilon$$ is an arbitrary small constant, we make the algorithm flexible to slackness. The algorithm thus automatically tunes itself to slackness degree and gives better performance than the optimal 2-competitive algorithm for real world inputs. In addition to worst-case competitive ratio analysis, a queueing model analysis is given and computer simulations are reported, confirming that the performance of the algorithm is high. We show how the approach can be generalized to a situation where the system has a number of intermediate states. Our model can be used to facilitate renewable energy integration into the electrical grid and we highlight that an online competitive approach can yield techniques for grid resiliency.

Most tidal energy site studies concentrate on resource size; few examine other factors that may be of equal or even greater importance in determining site feasibility for tidal turbine array deployments. These factors include aspects such as electrical grid proximity, water depth, the location of marine protected areas, pre-existing users, environmentally significant regions and other restrictions. Using these factors, a more holistic approach to site selection was conducted for Australian waters using a Multi-Criteria Evaluation (MCE) process. This MCE method uses standardized factors combined with weighted linear combinations to calculate a site suitability index, where each factor is multiplied by a weight of relative importance. A sensitivity analysis on the factor weighting was performed to ensure the suitable selection of weighting factors to reduce conformation bias. The developed MCE model identified three promising locations in Australia, with contiguous areas in regions to the north of Broome Western Australia of 534 km2, the Banks Strait region northeast of Tasmania of 67 km2, and the Clarence Strait located in the Northern Territory of

IN 1863, a single arc lamp on observatory Hill in Sydney, Australia, was lit to celebrate the marriage of Prince Albert of Wales and Princess Alexandra of Denmark. It was the first use of electricity anywhere in the country. It took 25 years until Australia established its first permanent 240-V electrical grid, in the small country town of Tamworth, New South Wales, in 1888. Two 18-kW, dc, coal-fired generators were supplied by the plentiful Gunnedah black coal basin nearby, and in the same year, on the other side of the continent, C.J. Otte supplied electricity to the Western Australian Government House with a small, 15-kW dynamo. By 1899, a full three-phase 240-V ac grid had been built on the east coast, establishing the foundation of the future power system across the country.

Environmental issues are a major concern in society. To solve them, new technologies have appeared in the las few years, such as Renewable Energy Sources (RES) or Electric Vehicles (EVs). Nevertheless, all these technologies have a huge impact in the Electric System, and especially, in the electric grid, due to their technical characteristics. In this sense, the grid needs to evolve to assume the new electric scenario. In this context, this paper is focused on summarizing how the grid should evolve to correctly integrate these new elements into de system with a special focus on EVs, as they will be the ones with the greatest affection on the load profiles. Hence, how to integrate EVs into the grid considering charging profiles will be explained in order to design the electric grid of the future.

Photovoltaic thermal and/or evacuated tube collectors on building roofs can be effectively used to reduce fossil fuel use for heating and reliance on the electrical grid. To evaluate the potential of this reduction, a set of models were created for rooftop photovoltaic thermal and evacuated tube collector energy production, both thermal and electricity, and tested using a series of potential layouts. Five collector area ratios, two layout options, and three working fluid flowrates were investigated using five reference buildings as case studies. From these case studies it was determined that in Toronto’s climate, the exclusive use of photovoltaic thermal collectors produces the most total energy, while using only evacuated tube collectors maximally offsets greenhouse gasses. The results suggest that district heating would be highly effective to reduce the carbon footprint of city cores like the Toronto 2030 District.

Electricity has become not merely a source of power but also a vital component of our lives in a rapidly changing world. However, according to a World Bank report, 17% of the globe’s population live without power. The key cause for such a huge population not being electrified is the proximity of the central electrical grid system or the high expenditure of installing the grid lines to such remote areas. The notion of a microgrid was first proposed more than a decade ago, but the numerous obstacles it entails have hampered its broad adoption and made it a research focus in recent years. A hierarchical control structure of the microgrid is designed, which is divided into layers according to the control objectives and control time scales of the microgrid, and the hierarchical control structure is realized by using multiagent technology. In the framework of hierarchical control, aiming at the demand of energy coordination and optimization of the microgrid, the operation strategy of the microgrid is proposed in grid-connected and/or off-grid mode. In the grid-connected mode, the large power grid is used for power supply and in the off-grid mode, the load bidding mechanism is introduced to ensure the power supply of important loads. Experiment results reveal the power quality and stability of the system.

Maintaining the quality and reliability of electricity transmission lines is central to effective energy governance. However, transmission line siting is often a contentious policy decision since permitting and constructing lines may involve private and public property, residents and communities, and localized and national concerns. Yet, policy conflict in transmission siting across cases and over time has remained largely understudied. This article derives and tests hypotheses about policy conflict in the context of transmission lines completed or constructed between 2017 and 2018 in the United States. In exploring the full population of transmission lines, we find that a majority exhibit relatively low and moderate levels of conflict and attention rather than high levels. We further examine a subset of six of these cases that represent a range of conflict and attention intensity. We describe variation in the diversity of actors and frames, advocacy coalitions, and the volume of discourse associated with transmission line siting over time. As problems related to energy governance have become more complex, energy siting disputes are likely to remain a fruitful area for research on policy conflict.

This chapter addresses the status of artificial intelligence (AI) as a central element in smart grid (SG) while focusing on the recent progress of research on machine learning techniques to pave the future work in the SG area. The SG framework provides a performance toolkit based on information and communication technologies. The SG paradigm supervises and promotes grid operations at a high level of expertise. AI systems have attributed to the realization of sustainable development goals including the vital integration of renewable energy sources (RES). The scarcity of conventional energy resources in the near future and their increasing threats to the environment extremely require the transition toward RES. The bulk penetration of RES into the electrical grid leads to unstable and volatile power generation. The chapter presents the commonly applied AI methods to the SG system and the key elements of the evaluation procedure.

THE PURPOSE. To consider and analyze the definitions of the concepts "configuration of the electrical grid", "control of the configuration of the electrical grid ", "parameters of the configuration of the electrical grid ". To justify the need to manage the configuration of electrical grid, describe the principles and methods of managing the configuration of the electrical grid. To demonstrate the implementation of the principles of managing the configuration of the electrical grid on the example of an electrical grid with a nominal voltage of 0.4 kV with the use of proprietary technical configuration management tools. METHODS. Based on the literature review, the study identified the wording of key concepts, such as" configuration of the electrical grid"," management of the electrical grid configuration", "parameters of the electrical grid configuration". Methods of managing the configuration of the electrical grid are shown. The principles of electrical grid configuration management proposed by the authors are considered, which include observability, manageability, segmentability, flexibility of the electrical grid configuration, equipment intellectualization, and integration into the economy. The essence of each of the management principles is revealed and the effects of their implementation are shown. The effects of implementing the configuration management principles are to reduce the number of power supply interruptions and reduce the time of power supply interruptions to consumers, mismatch the quality parameters of the electricity supplied to them, as well as to increase the availability of electricity infrastructure. The rationale for the need to manage the configuration of electrical grid is given. Within the framework of the conducted research, the method of system analysis, methods of comparisons and analogies, and expert assessments were used. RESULTS. On the example of the scheme of power supply to consumers at a voltage of 0.4 kV, the implementation of the principles of control of the configuration of electrical grid with the use of proprietary technical means – multi-contact switching systems, input-accounting-distribution devices integrated into the monitoring systems of the parameters of the operating modes of electrical grid and other devices is illustrated. CONCLUSION. The implementation of the principles of configuration management of electrical grid allows to increase the efficiency of power supply systems for consumers and at the same time causes the need to develop new technical means of automation and monitoring of parameters of operating modes of electrical grids.

This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.

The increasing presence of solar energy sources has radically transformed the continental electrical network. While solar panels affect the grid in a distributed manner, and an individual device has negligible weight, the presence of a large population engaged in energy production can affect the overall network dynamics. While this effect might in some scenarios be disruptive, solar panels can offer the potential for frequency regulation, if their power output is to be controlled. In this work, we present models for the aggregation of a heterogeneous population of solar panels connected to the electrical grid. The electrical network frequency is modeled centrally: the model accounts for primary frequency control, and its inertia is a function of the penetration of PV generation. We study how features of the PV population affect the overall frequency signal. In particular, we investigate scenarios of power production (infeed) incidents: we simulate sudden generation losses and study the corresponding frequency response at varying levels of renewable penetration. Simulations show good performance of the aggregated models when compared with the physical system and quantitatively indicate that population heterogeneity is a desirable feature to avoid load shedding. We furthermore put forward a control architecture to regulate the power generation of the PV population: this intentionally simple architecture is aligned with primary control and is tailored to a decentralized implementation. We show that the control scheme can avoid load shedding, despite increasing penetration of solar generation, thus contributing to network resilience.

Renewable energy sources (RES) are being integrated to electrical grid to complement the conventional sources to meet up with global electrical energy demand. Among other RES, Wind Energy Conversion Systems (WECS) with Doubly Fed Induction Generator (DFIG) have gained global electricity market competitiveness because of the flexible regulation of active and reactive power, higher power quality, variable speed operation, four quadrant converter operation and better dynamic performance. Grid connected DFIG-based WECS are prone to disturbances in the network because of direct connection of stator windings to grid. The ability of the Wind Turbine (WT) to remain connected during grid faults is termed the Fault Ride-Through (FRT) capability. The grid code requirement for integrating the DFIG-based WTs to power networks specified that they must remain connected and support the grid stability during grid disturbances of up to 1500 ms. The use of compensation devices offers the best FRT compliance thereby protecting the DFIG and the converters from voltage fluctuations and over currents during the grid fault. The paper presents a review of techniques employed in ensuring FRT compliance. The article also proposes the state-of-the-art techniques for compensating voltage sag/swell and limiting the fault short-circuit current. Keywords: Renewable energy sources, DFIG, wind turbine system, fault ride-through, grid codes, dual-functional DVR

In this study, we present a master–slave methodology to solve the problem of optimal power dispatch in a direct current (DC) microgrid. In the master stage, the Antlion Optimization (ALO) method solves the problem of power dispatch by the Distributed Generators (DGs); in the slave stage, a numerical method based on successive approximations (SA) evaluates the load flows required by the potential solutions proposed by the ALO technique. The objective functions in this paper are the minimization of energy production costs and the reduction of $$\hbox {CO}_2$$ emissions produced by the diesel generators in the microgrid. To favor energy efficiency and have a lower negative impact on the environment, the DC microgrids under study here include three DGs (one diesel generator and two generators based on renewable energy sources, i.e., solar energy and wind power) and a slack bus connected to a public electrical grid. The effectiveness of the proposed ALO–SA methodology was tested in the 21- and 69-bus test systems. We used three other optimization techniques to compare methods in the master stage: particle swarm optimization, continuous genetic algorithm, and black hole optimization. Additionally, we combined SA with every method to solve the load flow problem in the slave stage. The results show that, among the methods analyzed in this study, the proposed ALO–AS methodology achieves the best performance in terms of lower energy production costs, less $$\hbox {CO}_2$$ emissions, and shorter computational processing times. All the simulations were performed in MATLAB.

This paper evaluates the performance of two arrangements of interphase transformers using a new AC–DC 48-pulse converter (SC-48P) for industrial applications with high currents in the order of hundreds of kiloamperes, so that consumers and other loads connected to the SC-48P electrical grid are not affected by the harmful effects caused by harmonic currents injected into the grid by converters. The prototype uses a $$7.5^{\circ }$$ and 4 kVA-220/220 V three-phase phase-shifting autotransformer and two groups of 24-pulse converters, each containing two 2 kVA-220/180/180 V identical three-phase three-winding non-conventional transformers with four 6-pulse converter bridges connected in parallel. The methodology for calculating the inductance for each IPT is given using the mathematical formulation of AC currents established and processed using the MathCad software. Computational simulations in normal and degraded modes were conducted in MATLAB/Simulink to prove the prototype’s performance in terms of harmonic distortion and reliability, considering only three operational scenarios. The SC-48P was experimentally tested using available laboratory resources to validate the research. The obtained results meet IEEE-519 requirements and confirm the viability and applicability of the proposed converter in industrial rectification systems in terms of power quality improvements.

The current research study proposes a multi-objective optimal power flow (OPF) solution using a modified Interior Search Algorithm in which Levy Flight feature with two different strategies is incorporated to accelerate the convergence speed and to enhance solution quality. In traditional OPF problems, the thermal generation units alone are accounted, whereas the security challenges faced by the network are mostly ignored. In other terms, the emission needs to be significantly reduced in terms of environmental sustainability aspects. So, the electrical grid must be infused with power generated from different renewable energy sources. Consequently, the current research article proposes an approach in order to accomplish OPF through a combination of stochastic wind and solar power coupled with traditional thermal power generators in the system. The authors leveraged modified IEEE 30-bus system, IEEE 118-bus system and real-time electrical network 62-bus Indian Utility System in order to validate the Levy Interior Search Algorithm proposed in the study by incorporating renewable energy sources. During implementation, the researchers considered different factors such as network security limitations, for instance transmission line capacity, bus voltage limits and restricted operation zones for thermal units. The simulation results obtained using the proposed LISA Strategy-II algorithm are compared with the results obtained using LISA Strategy-I, ISA and other optimization algorithms reported in the literature. The results achieved from the implementation infer that the proposed method has inherently good convergence characteristic and affords better exploration of the Pareto front.

The fixed public charging stations (FCS) network is challenged by widespread of electric vehicle (EV) uses. Therefore, there is exploitation of the many parks spread over the territory of a smart city by means of mobile charging stations (MCS). That can be set up or moved anywhere as needed. This allows for the rapid expansion of the charging infrastructure. In this work, we propose an architecture system consisting of a set of algorithms to manage electric vehicle charging plans in terms of minimizing journey time, including waiting and charging time at charging stations (CS). Thus, During the CS selection decision, the system takes into consideration the amount of sufficient energy for the EV to reach the specified CS, the remaining amount of energy in stock if the selected CS is the MCS type, the CS Real-time status, and the first-come-first-served policy based on providing charge seats in CS. Moreover, the dynamically system regulates each FCS at its peak time of its MCS operation, ensuring a semi-permanent equilibrium in electrical grid usage and reducing congestion by changing the flow of vehicles that are directed towards FCSs for charging. The evaluation results demonstrate, in the context of the Helsinki City scenario, the effectiveness of the proposed system and algorithms, in terms of achieving the above-mentioned objectives.

While the past 50 years have been dominated by the so-called communication revolution, today, we are at the dawn of the energy revolution. The entire economy is energy driven, being electricity its most important energy vector. By the end of 2040, electricity is expected to contribute up to 40% of the increase in final energy consumption, and concepts, such as smart grid or internet of energy, are now commonplaces in our lexicon. The Paris UN climate change conference agreement has imposed three major changes into the electrical grid paradigm: the need to reduce carbon emissions; the need to decentralize electrical energy production, and the need to increase transport electrification.

To address the new challenges arising from the higher penetration of renewable energy in electrical grid, Demand Response (DR) aims to involve the residential consumers in the grid equilibrium. Ensuring benefits for both utility and users requires the consumers sensitivities to be understood and then included in the Energy Management System (EMS). For this purpose, the cost is the predominant and most often only factor taken into account in the literature, although in the residential sector other concerns influencing electricity consumption behaviour have been observed. This paper presents a two levels EMS applied to a neighbourhood of consumers mathematically modelled at the level of their appliances and incorporating 5 consumers profiles along three sensitivities: cost, environment and appliances shifting comfort. The first level is a day ahead supervision based on a multi-agent optimisation lead by a central aggregator but performed locally by the household using Dynamic Programming (DP), thus ensuring privacy protection for the stakeholders. The second level is a real time supervision using the same decentralised structure and based on fuzzy logic. Both levels are evaluated in this paper, with a focus on the balance between grid and consumers objectives.

Recent trends in the utilization of renewable and sustainable energy sources have led to an increased penetration of inertialess power electronics-based energy resources into the electrical grid. The concept of the virtual synchronous generator (VSG) has recently been studied to overcome the drawbacks of the fast-responding inertialess inverter by mimicking the behavior of a traditional synchronous generator. The majority of literature on VSGs assumes the operation of VSGs in inductive networks; however, such control algorithms do not operate well in a more resistive network such as a low-voltage distribution network. This article introduces a new neural network-based predictive control for VSGs that is capable of operating optimally in both inductive and resistive networks by optimizing the total tracking error during transients. After the introduction of the control scheme, simulation and experimental results are provided to evaluate the effectiveness of the proposed algorithm in reducing oscillations and settling time.

The problem of energy management in the smart autonomous electrical grids (SAEGs) is a main challenge in the active distribution networks. In such systems, the operator of the network decides on the optimal scheduling of the resources to supply the local demand. In this paper, a multi-objective optimization model is developed for a SAEG considering responsive consumers (RCs) and a hydrogen storage system (HSS). The objective functions are maximizing the reliability and minimizing both the operation cost and the gap between the energy consumption and its optimal value. The participation of the RCs is modeled through the demand shifting strategy and the local generation of the plug-in electric vehicles. To model the uncertainties of the renewable energy sources and the demand, the Monte Carlo simulation approach is used. The resulted model is solved using the shuffled frog leaping algorithm (SFLA) regarding which the non-dominated solutions are generated. Then, the best solution is obtained using the fuzzy and the weighted sum methods. To investigate the effectiveness of the proposed model, it is applied on a 24-node test system through defining four case studies. The results shown that in the presence of the RCs and the HSS, the operation cost and the reliability of the system both improve.

The potential for critical infrastructure failures during extreme weather events is rising. Major electrical grid failure or "blackout" events in the United States, those with a duration of at least 1 h and impacting 50,000 or more utility customers, increased by more than 60% over the most recent 5 year reporting period. When such blackout events coincide in time with heat wave conditions, population exposures to extreme heat both outside and within buildings can reach dangerously high levels as mechanical air conditioning systems become inoperable. Here, we combine the Weather Research and Forecasting regional climate model with an advanced building energy model to simulate building-interior temperatures in response to concurrent heat wave and blackout conditions for more than 2.8 million residents across Atlanta, Georgia; Detroit, Michigan; and Phoenix, Arizona. Study results find simulated compound heat wave and grid failure events of recent intensity and duration to expose between 68 and 100% of the urban population to an elevated risk of heat exhaustion and/or heat stroke.

Traditional generating units, as well as renewable energy resources, make up the electrical grid. The proposed article proposes performance indices for optimal power flow, which combine wind turbin...

Electrical energy becomes necessary for human being. Generation of electrical energy mostly depends on fossils fuel, they are limited in nature and also responsible for environmental pollution. Renewable energy resources provides a better alternative for future. In comparison to conventional energy resources economical aspect is a major issue of renewable energy sources with the feasibility and efficiency. This paper investigates the performance analysis and control of Photovoltaic (PV)-wind hybrid system connected to electrical grid and feeds large plant with critical variable loads. The technique of extracting maximum power point is applied for the hybrid power system to capture maximum power under varying climatic conditions. Moreover, Control strategy for power flow is proposed to supply critical load demand of plant. The Dynamic performance of the proposed hybrid system is analyzed under different environmental conditions. The simulation results have proven the effectiveness of the proposed maximum power point tracking (MPPT) strategies in response to rapid variations of weather conditions during the day.

Distributed energy resources (DERs) are transforming the operation and planning of both distribution and transmission grids. Stability impacts from the increasing generation contribution of DERs have been widely recognized in distribution systems and are emerging in transmission systems. There is a need for the development of transmission hosting capacity studies of DERs so utilities will be able to predict and prevent instabilities, reductions in reliability on the electrical grid, and create planning methodologies that enable the continued growth of DERs. This paper evaluates the impact of different modeling considerations for assessing hosting capacity of DERs in transmission systems through analysis on a 2,000-bus synthetic grid test system. The modeling considerations include transient and steady state contingencies, use of dynamic load and DER models with voltage support control, dynamic load composition, and seasonal and loading scenarios. The results demonstrate that transient stability evaluations are more limiting than steady state analysis, the use of voltage support control in DERs can increase hosting capacity in the system, and large variations in hosting capacity can be found when assessing between seasonal variations. From comparing system factors the amount of wind generation in the system was a critical factor for hosting capacity in this study.

Detailed simulation of wind generation as driven by weather patterns is required to quantify the impact on the electrical grid of the power fluctuations in offshore wind power fleets. This paper focuses on studying the power fluctuations of high-installation-density offshore fleets since they present a growing challenge to the operation and planning of power systems in Europe. The Belgian offshore fleet is studied because it has the highest density of installation in Europe by 2020, and a new extension is expected to be fully operational by 2028. Different stages of the future installed capacity, turbine technology, and turbine storm shutdown technologies are examined and compared. This paper analyzes the distribution of power fluctuations both overall and during high wind speeds. The simulations presented in this paper use a new Student t-distributed wind speed fluctuation model that captures the missing spectra from the weather reanalysis simulations. An updated plant storm shutdown model captures the plant behavior of modern high-wind-speed turbine operation. Detailed wake modeling is carried out using a calibrated engineering wake model to capture the Belgium offshore fleet and its tight farm-to-farm spacing. Long generation time series based on 37 years of historical weather data in 5 min resolution are simulated to quantify the extreme fleet-level power fluctuations. The model validation with respect to the operational data of the 2018 fleet shows that the methodology presented in this paper can capture the distribution of wind power and its spatiotemporal characteristics. The results show that the standardized generation ramps are expected to be reduced towards the 4.4 GW of installations due to the larger distances between plants. The most extreme power fluctuations occur during high wind speeds, with large ramp-downs occurring in extreme storm events. Extreme ramp-downs are mitigated using modern turbine storm shutdown technologies, while extreme ramp-ups can be mitigated by the system operator. Extreme ramping events also occur at below-rated wind speeds, but mitigation of such ramping events remains a challenge for transmission system operators.

One of the core necessities for development in the modern world is a clear access to energy. Therefore, places that lack access to energy face serious challenges on their ability to progress effectively. In the bid to progress, nations have utilized conventional energy sources for decades leading to plummeting natural oil levels and increasing global temperatures, not to mention climate change. Energy transitions have begun to catch up but the world is far from creating a decisive decline in global emissions. Next to transportation, electricity generation burns the highest amount of fossil fuels today. The losses incurred during transmission and distribution exacerbate existing problems. One viable solution is to interlink local sustainable energy generation plants to existing grids. As of 2019, 583.5 GW of operation photovoltaic energy supplies our demands. Solar prosumage development promises to boost this rise in PV usage. However, it is difficult as it is to manage conventional grids let alone interlinked smart grids. Unlike classical approaches to developing electrical grid lines, modern systems demand much more in terms of tangible resources, statistical analysis and computational techniques. This level of complexity has inspired engineers to develop Artificial Intelligence techniques that can develop deep neural networks that grow robustly, while maintaining flexibility in handling non-linear complex relationships within large sets of data. In this paper, we provide a systematic study of existing AI solutions for smart solar grids and discuss the possible challenges associated with implementing this technology in near future.

Large-scale integration of renewable energies into the electrical grid will remain limited until high-capacity storage systems are developed to modulate the intermittency of harvestable sources. Th...

Interdependent critical infrastructures in coastal regions, including transportation, electrical grid, and emergency services, are continually threatened by storm-induced flooding. This has been demonstrated a number of times, most recently by hurricanes such as Harvey and Maria, as well as Sandy and Katrina. The need to protect these infrastructures with robust protection mechanisms is critical for our continued existence along the world’s coastlines. Planning these protections is non-trivial given the rare-event nature of strong storms and climate change manifested through sea level rise. This article proposes a framework for a methodology that combines multiple computational models, stakeholder interviews, and optimization to find an optimal protective strategy over time for critical coastal infrastructure while being constrained by budgetary considerations.

The legalization of cannabis has caused a substantial increase in commercial production, yet the magnitude of the industry’s environmental impact has not been fully quantified. A considerable amount of legal cannabis is cultivated indoors primarily for quality control and security. In this study we analysed the energy and materials required to grow cannabis indoors and quantified the corresponding greenhouse gas (GHG) emissions using life cycle assessment methodology for a cradle-to-gate system boundary. The analysis was performed across the United States, accounting for geographic variations in meteorological and electrical grid emissions data. The resulting life cycle GHG emissions range, based on location, from 2,283 to 5,184 kg CO2-equivalent per kg of dried flower. The life cycle GHG emissions are largely attributed to electricity production and natural gas consumption from indoor environmental controls, high-intensity grow lights and the supply of carbon dioxide for accelerated plant growth. The discussion focuses on the technological solutions and policy adaptation that can improve the environmental impact of commercial indoor cannabis production. As cannabis production becomes legalized and legitimized, its production will likely change and expand with attendant environmental impacts. This life cycle analysis of energy and material costs across the United States focuses on indoor cannabis growing operations.

In this paper, the method of digital twin integrated power-hardware-in-the-loop for the assessment of distributed renewable energy resources is presented. The digital twin of the electrical grid is created via synchronization between the real-time simulator and the supervision, control and data acquisition system. The digital twin can be integrated to larger system with the addition of simulated devices, with or without reconfiguration of topology. The setup is then completed with power-hardware-in-the-loop interface where the real distributed energy resource can be physically integrated. The impact can then be analysed locally and globally as it propagates through the desired deployment environment.

The electrical grid is evolving from a network consisting of mostly synchronous machines to a mixture of synchronous machines and inverter-based resources such as wind, solar, and energy storage. This transformation has led to a decrease in mechanical inertia, which necessitate a need for the new resources to provide frequency responses through their inverter interfaces. In this paper we proposed a new strategy based on model predictive control to determine the optimal active-power set-point for inverters in the event of a disturbance in the system. Our framework explicitly takes the hard constraints in power and energy into account, and we show that it is robust to measurement noise, limited communications and delay by using an observer to estimate the model mismatches in real-time. We demonstrate the proposed controller significantly outperforms an optimally tuned virtual synchronous machine on a standard 39-bus system under a number of scenarios. In turn, this implies optimized inverter-based resources can provide better frequency responses compared to conventional synchronous machines., Submitted to IEEE Transactions on Power Systems

General-compensation-purpose Static var Compensator Prototype

Providing of energy is one of the most important issues for each country. Also, environmental issues due to fossil fuel depletion are other serious concern of them. In this regard, moving toward energy sustainability is a constructive solution for each country. This paper studies the short-term planning of generating units in renewable energy-based distribution networks equipped with plug-in electric vehicles (PEVs). PEVs can cause problems for distributed energy sources in the electrical grid, as well as power units inside the grid. So, to overcome this problem, an efficient stochastic programming technique is designed to allow the control entity to control the charging behavior of PEVs for managing power units. In this paper, to obtain the least total cost, a new method is suggested to decrease the reliability expenses. In other words, the vehicle-2-grid (V2G) is applied to decrease the operating. On the other hand, a novel stochastic flow using the unscented transform is suggested to improve the model of the severe uncertainty due to the wind power, photovoltaic (PV) and charging/discharging power of PEVs. In this research work, a novel and efficient optimization algorithm called ‘θ-modified krill herd (θ-MKH)” is used as an applicable technique to optimize the microgrid (MG) operation. This algorithm is useful and has many advantages like the runaway from the local optima with fast converging in comparison with other methods. Also, the satisfactory efficiency of the suggested randomized manner is validated on an MG connected to the main grid.

Electricity consumption by air-conditioners in residential buildings significantly affects the electrical grid. The stability of the grid can be improved by a precise prediction of air-conditioning (AC) variations. Therefore, more information about the electricity usage patterns specifically on AC load is important. This paper aims to determine the consumption patterns of AC and total load through a field measurement of 20 dwellings located in Kuala Lumpur and Selangor. Next, correlation and clustering are employed to identify the relationship between outdoor temperature and AC electricity consumption. The results revealed 75% of the occupants use the AC almost every day for about five to six hours per day at the rates of 0.93 kWh/day during day time and 3.43 kWh/day during night time. The correlation coefficient between the total daily AC consumption and the mean daily outdoor temperature ranged from -0.1 to 0.48. The variation of electricity consumption with time shows that AC electricity consumption increases with the increase of usage time. Lastly, the clustering process of AC usage pattern based on outdoor temperature displayed the uncommon behaviour from dwellings whereby high AC electricity consumption was recorded at low outdoor temperature.

The tourism sectors consume a high amount of electrical power, which in most cases is supplied by the electrical grid and diesel generator. As a result, the electricity supply of large-scale tourism sectors is an important challenge in many countries. In current study, the feasibility study for applying the hybrid renewable energy systems to provide the power demand in social areas is investigated. Since Iran has a lot of tourism areas with a high amount of electricity consumption, a popular and crowded tourism sector in the Qom (a city in the center of Iran) is selected as the case study. The electricity consumption of this social area is found to be 23,808.91 kWh/day, which in the current system is supplied by the electricity grid. Also, the values of cost of energy and carbon dioxide emissions of current system are calculated to be 0.0931$/kWh and 5,741,214 kg/year, respectively. Regarding the available renewable resources of Qom city and the space limitations of this tourism sector, several combined renewable scenarios are simulated and assessed applying the HOMER Pro simulator. In continue, to identify the optimal scenarios, the techno–economic–environmental evaluation is implemented. The hybrid photovoltaic (PV)/wind turbine (WT)/battery (Bat)/diesel generator (DG)/biogas generator (BG), and grid/PV/WT are found as the optimum configurations for the off-grid and grid-connected systems, respectively. It was found that the Grid/PV/WT has a net present cost of 6.63 M$ and a cost of energy of 0.0234 $/kWh. Also, the cost of energy and net present cost of hybrid PV/WT/Bat/DG/BG are accounted for 0.1251$/kWh and 18.1 M$, respectively. By using this stand-alone system instead of current system, the CO2 emissions reduce by 61.87% (2,189,231 kg/year). In following, to assess the effects of load growth and PV degradation on system performance, the multi-year module of this software is employed. Based on the obtained findings, by applying the hybrid PV/WT/Bat/DG/BG instead of the current system, the contribution of emissions penalties to the overall net present cost diminishes from 15.47% to 0.73%. Finally, the heat map sensitivity analyses are applied to the economic and environmental parameters.

In recent times, improvement in building designs has been on a steady upward trend, precipitated by large greenhouse gas emissions of energy draining outmoded construction materials. Global energy crisis exacerbated by climate change has motivated the design and construction of energy-efficient Net Zero Energy Buildings (NZEB). This paper proposes an energy management strategy for grid-connected Net Zero Energy Buildings that help to achieve a net zero balance of energy in Electrical Grid Photovoltaic-connected NZEB homes. The significance of NZEB was evaluated by providing insights into cost-savings for the consumer’s net energy consumption and reduced carbon footprint. The proffered energy management strategy uses a single-objective Differential Evolution (DE) optimization algorithm that prunes the demand for electrical energy through an efficacious appliance scheduling routine. A novel mathematical energy index is introduced that enables consumers to monitor the net energy imported from the electrical grid and maintain a zero energy index. The proposed net zero energy concept is validated using DE and Genetic Algorithm (GA)-based optimization techniques.

Buildings can be operated in an energy-flexible manner while respecting occupant thermal comfort. This energy flexibility of building operations, both in time and quantity, can be harnessed by the electrical grid for load balancing. In the context of smart grid and intelligent buildings, the concept of energy flexibility in buildings broadens the existing demand management thinking from the top-down one-way control to two-way communications. This paper, extending studies on thermostat controls of heating and air conditioning systems for demand response, evaluates the impact of different control schemes on the energy flexibility of residential buildings. Two control strategies, Model Predictive Control (MPC) and Rule-Based Control (RBC), are investigated for a space heating system using co-simulation studies. Four indicators are introduced and adapted from the literature to assess the control performances of the strategies. Simulation results show that different flexibility indicators favour different control strategies in this case study. For demand response events of four hours, the MPC strategy presents two to three times of flexible energy than that of RBC. MPC also delivers 20% more of maximum power reduction during the events against RBC. The RBC strategy, on the other hand, is twice of MPC for flexible energy efficiency. This evaluation work can be beneficial to guide the control system design of new buildings or control retrofits of existing buildings that consider better grid-building interactions for the future.

The reduction of CO2 emissions has high priority in many parts of the world. It is achieved primarily through the expansion of renewable energies such as PV and wind power. However, they have the disadvantage of fluctuating over time. Energy flexibility measures (EFM) can provide the necessary compensation. One measure is the installation of bivalent and hybrid facilities. They can change their energy source to reduce energy costs, stabilize the electrical grid, and increase efficiency, e.g. for economic and ecological considerations, the waste heat potential and its utilization must be taken into account. EEP and Fraunhofer IPA have developed a method to evaluate various waste heat recovery technologies, such as thermal storage, ORC plants, and other technologies depending on their energy flexibility and their potential to reduce the facility’s energy costs. In addition, the change in the electricity to gas price ratio on the level of energy cost savings was examined in a parameter study. The developed method was applied to a bivalent furnace.