System Load Profile Research Articles

Power System Integration of Electric Vehicles: A Review on Impacts and Contributions to the Smart Grid

In recent years, electric vehicles (EVs) have become increasingly popular, bringing about fundamental shifts in transportation to reduce greenhouse effects and accelerate progress toward decarbonization. The role of EVs has also experienced a paradigm shift for future energy networks as an active player in the form of vehicle-to-grid, grid-to-vehicle, and vehicle-to-vehicle technologies. EVs spend a significant part of the day parked and have a remarkable potential to contribute to energy sustainability as backup power units. In this way, EVs can be connected to the grid as stationary power units, providing a range of services to the power grid to increase its reliability and resilience. The available systems show that EVs can be used as alternative energy sources for various network systems like smart grids, microgrids, and virtual power plants besides transportation. While the grid–EV connection offers various contributions, it also has some limitations and effects. In this context, the current study highlights the power system impacts and key contributions of EVs connected to smart grids. Regarding the power system impacts in case of EV integration into smart grids, the challenges and difficulties are categorized under the power system stability, voltage/current distortions, load profile, and power losses. Voltage/current distortions like sags, unbalances, harmonics, and supraharmonics are also detailed in the study. Subsequently, the key contributions to the smart grid in terms of energy management, grid-quality support, grid balancing, and socio-economic impacts are explained. In the energy management part, issues such as power flow, load balancing, and renewable energy integration are elaborated. Then, the fault ride-through capability, reactive power compensation, harmonic mitigation, and grid loss reduction are presented to provide information on power quality enhancement. Lastly, the socio-economic impacts in terms of employment, net billing fees, integration with renewable energy sources, and environmental effects are elucidated in the present study.

Optimization of Wind Farm Layout using Genetic Algorithms

In order to increase the economic feasibility, sustainability, and efficiency of energy production, this research proposes an improved optimization framework for hybrid wind-solar energy systems that use an augmented Genetic Algorithm (GA). Wind turbine size and photovoltaic (PV) panel orientation were optimized using historical data on wind and solar resources, system load profiles, and component specifications. There was an 18% increase in energy production, a 14% improvement in wind turbine efficiency, and a 16% increase in solar panel output because to the GA's outstanding performance. An 18% reduction in the payback time and a 12% reduction in the Levelized Cost of Energy (LCOE) were achieved. Results from the evaluation of the project's social and environmental consequences showed that community acceptability increased by 9 percentage points and land-use efficiency by 12 percentage points. A sensitivity study verified that the system could withstand several economic and environmental scenarios. The results demonstrate the promise of GA-based optimization in improving the efficiency of renewable energy hybrid systems.

A Cooperative Hierarchical Multi-Agent System for EV Charging Scheduling in Presence of Multiple Charging Stations

The increasing penetration of plug-in electric vehicles (EVs) to the electrical grid raises concerns over secure and economic operation of the system. A coordination mechanism between system operator and EV aggregators is necessary to ensure that the system is operated within the security limits, and to reduce the charging costs while satisfying EV users’ energy needs. In this work, we present a cooperative hierarchical multi-agent system and propose an EV charging scheduling strategy in order to minimize the demand and energy charges while meeting the EV users’ energy requirements and satisfying the system security constraints. Within the designed framework, the higher-level agents calculate a set of proposed control signals by solving the designated optimization problems, and send them to the lower-level agents to facilitate an optimal scheduling in line with the aforementioned objectives. Through this hierarchically distributed approach, it is possible to effectively coordinate multiple EV charging stations without the need of direct communication or any prior information related to EV arrivals. The computational complexity of the problem is reduced by distributing the work among agents, and the privacy of sensitive data, such as system topology, load profiles, and EV parameters, is preserved. Moreover, unlike the traditional distributed solution methods that converge iteratively, the proposed approach calculates the optimal charging schedule after a single round of communication. The efficacy of the proposed methodology is demonstrated by a series of case studies on 33-bus and 118-bus distribution test feeders.

Comparative analysis of dynamic pricing schemes in distributed energy management of residential users in smart grid

Increasing power demand, greenhouse gas emissions, and the old infrastructure are serious concerns in the existing power system. With the advent of the smart grid, demand response (DR) has emerged as an effective approach to handle these issues. The selection of an appropriate DR program is vital to acquire the maximum benefits for the utility and the consumers. In this context, a distributed energy management scheme for residential consumers is presented and analyzed to observe the impact of different pricing schemes. The three dynamic pricing schemes considered in this work are based on linear function, the logarithmic function, and the penalty-based linear function of aggregated load. A non-cooperative game is used to formulate the energy management problem of the consumers. The Nash equilibrium of the game is obtained using the proximal decomposition algorithm. The results are obtained for different cases based on the presence of a storage device, a dispatchable generation unit, and two different modes of operation of an electric vehicle. The best pricing scheme is chosen based on the minimum cost, the peak-to-average ratio of the system load profile, and consumer comfort.

A non-cooperative game based energy management considering distributed energy resources in price-based and incentive-based demand response program

Abstract Demand response (DR), an integral part of the smart grid, has great potential in handling the challenges of the existing power grid. The potential of different DR programs in the energy management of residential consumers (RCs) and the integration of distributed energy resources (DERs) is an important research topic. A novel distributed approach for energy management of RCs considering the competitive interactions among them is presented in this paper. The impact of participation of RC’s in price-based (PB) and incentive-based (IB) DR programs is investigated using game theory. For this, an energy management optimization problem (EMOP) is formulated to minimize electricity cost. The utility company employs electricity price as a linear function of aggregated load in the PB DR program and an incentive rate in the IBDR program. RCs are categorized into active and passive users. Active users are further distinguished based on the ownership of energy storage devices (SD) and dispatchable generation units (DGU). EMOP is modeled using a non-cooperative game, and the distributed proximal decomposition method is used to obtain the Nash equilibrium of the game. The results of the proposed approach are analyzed using different case studies. The performance of the proposed approach is evaluated in terms of aggregated cost and system load profile. It has been observed that participation in PB and IBDR program benefits both the utility and the consumers.

Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs

Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.

Recent Challenges and Methodologies in Smart Grid Demand Side Management: State-of-the-Art Literature Review

The concept of smart grid was introduced a decade ago. Demand side management (DSM) is one of the crucial aspects of smart grid that provides users with the opportunity to optimize their load usage pattern to fill the gap between energy supply and demand and reduce the peak to average ratio (PAR), thus resulting in energy and economic efficiency ultimately. The application of DSM programs is lucrative for both utility and consumers. Utilities can implement DSM programs to improve the system power quality, power reliability, system efficiency, and energy efficiency, while consumers can experience energy savings, reduction in peak demand, and improvement of system load profile, and they can also maximize usage of renewable energy resources (RERs). In this paper, some of the strategies of DSM including peak shaving and load scheduling are highlighted. Furthermore, the implementation of numerous optimization techniques on DSM is reviewed.

Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders

In this paper, a coordination method of multiple electric vehicle (EV) aggregators has been devised to flatten the system load profile. The proposed scheme tends to reduce the peak demand by discharging EVs and fills the valley gap through EV charging in the off-peak period. Upper level fair proportional power distribution to the EV aggregators is exercised by the system operator which provides coordination among the aggregators based on their aggregated energy demand or capacity. The lower level min max objective function is implemented at each aggregator to distribute power to the EVs. Each aggregator ensures that the EV customers’ driving requirements are not relinquished in spite of their employment to support the grid. The scheme has been tested on IEEE 13-node distribution system and an actual distribution system situated in Seoul, Republic of Korea whilst utilizing actual EV mobility data. The results show that the system load profile is smoothed by the coordination of aggregators under peak shaving and valley filling goals. Also, the EVs are fully charged before departure while maintaining a minimum energy for emergency travel.

Optimal integration of multi-type DG in RDS based on novel voltage stability index with future load growth

In this article, a novel voltage stability index is proposed for the optimal placement and an analytical as well as particle swarm optimization method is implemented for optimal sizing of different types of distributed generation (DG) in a radial distribution system. The three types of DG considered are Type I, Type II and Type IV model. The methods account for changes in long-term system load profile in the planning phase to enhance power system performance. The load model chosen is composite. The optimal power factor and the cost of the DG are also considered. The efficiency of the proposed technique is tested and validated on IEEE 33-bus and 69-bus systems. The significant savings in annual energy loss, reduction in system power losses with upgraded voltage profile is observed for Type IV DG at optimal PF over other types. The results attained are further compared to other analytical and nature-inspired methods to exemplify the dominance of the proposed methodology. The statistical analysis has also been carried out for both bus systems without and with considering all types of DG.

Different charging infrastructures along with smart charging strategies for electric vehicles

The accelerating energy demand, growing concern regarding global warming and climate change has paved the path of electrification of the transport sector. Large scale adoption of Electric Vehicles (EVs) call for availability of sustainable and easily accessible charging infrastructure. The sporadic energy demand, different battery storage capacity and diverse penetrating patterns of electric vehicles have significantly raised the load elasticity on a power grid. Smart-grid environment promises to assist the addition of EVs into national grids by enabling both EV-charging and discharging (G2V and V2G) load. This will modify the load profile and reduce cost.This paper discusses comprehensively three basic infrastructures by which charging of EVs can be done. These infrastructures are studied and compared on the basis of some parameters. It has been found that distributed infrastructure shows best results for the charging of electric vehicles. The other two infrastructures prove costlier and increase power demand. Also, this paper examines three specific smart charging strategies and the impact of each strategy on the power system load profile and realization cost. Simulation results establish the superiority of smart charging over dumb charging.

Comparative Analysis of IGBT Parameters Variation Under Different Accelerated Aging Tests

Power semiconductor devices are vulnerable to thermomechanical fatigue due to temperature cycling caused by system load profile and external climatic conditions. As the reliability performance of power semiconductor devices in power converter systems is determined by this temperature stress, accelerated power or thermal cycling test are used in this article to emulate the temperature stress. The failure mechanisms due to temperature stress can be studied, and it also helps to develop the lifetime estimation model for power semiconductor devices. In the past, active power cycling and passive thermal cycling tests are investigated separately to develop the failure mechanism model based on the device’s electrical parameter changes and scanning electron microscopic (SEM) analysis. However, there is no detailed report on, how different temperature stresses emulated by dc, pulsewidth modulation (PWM) power cycling, and thermal cycling affects the device physically, and how much changes it introduces in the device’s electrical parameters. Therefore, this article presents an experimental investigation of active dc, PWM power cycling and passive thermal cycling approaches on 1.2-kV 50-A fast trench insulated gate bipolar junction transistor (IGBT) devices. The device electrical parameters, such as ON-state voltage, threshold voltage, gate current, gate voltage, and thermal resistance, were monitored during the study to classify the effects of temperature stress produced by dc, PWM power cycling, and thermal cycling on these electrical parameters.

Multi-Objective Demand Side Management at Distribution Network Level in Support of Transmission Network Operation

This paper proposes a methodology for advanced demand side management (DSM) in the distribution network (DN), catering at the same time for the requirements of the network operator, transmission or distribution, the available flexibility of the demand side, and the preservation of network performance. The basic premise of the study is that the distribution network operator is providing flexibility to the transmission system operator through load shaping. Different, one or more, indicators can be chosen to assess preservation of network performance. In this paper, for illustrative purposes of the methodology, the steady state voltage stability index, namely the load margin, is chosen as the network performance indicator. This is evaluated before and after a DSM action, in order to analyse the possible effect of DSM on network loadability. Load at each DN bus is represented using a realistic composite load model comprising controllable and uncontrollable loads. The DSM is initially performed based on optimal power flow calculation to ensure that the distribution system load profile at the grid supply point follows the required (pre-specified) load profile during a 24 hour period. Following this, a particle swarm optimisation is used to modify DSM program in those time steps of the planning horizon where load margin is reduced. The methodology is illustrated on a number of case studies using modified IEEE 33 bus distribution network model. The results illustrate the extent to which the available flexibility of demand can be used in support of transmission network operation when the preservation of network performance and appropriate load modeling are considered.

Cost and Availability Improvements for Fault-Tolerant Systems Through Optimal Load-Sharing Policies

The demand for highly-available systems is continuously increasing. Fault-tolerant systems which are composed of several redundant sub-units allow for continuous operation of such critical systems by distributing the system load across the functional units. However, these systems can fail if several redundant sub-units break down simultaneously. To address this, we propose a modeling and simulation approach to study the probability of simultaneous breakdown of redundant sub-units under different load distribution strategies. This allows for choosing the load-sharing strategy which achieves the highest availability for a certain system load profile and operational lifetime. We show that different load-sharing strategies can change the system availability and the operational and maintenance costs by almost an order of magnitude for a redundant power-supply unit of industrial controllers.

Electric Vehicle Scheduling Considering Co-optimized Customer and System Objectives

Efficient electric vehicle (EV) scheduling is a multi-objective optimization problem with conflicting customer and system operator interests, especially during vehicle-to-grid implementations. Economic charging while minimizing battery degradation and maintaining system load profiles couple the interests of these two entities. This paper focuses on identifying the relationships between these objectives and proposes to use an augmented epsilon-constrain (AUGMECON) based technique to implement two-way and three-way multi-objective optimizations. The importance of using these objectives in peak-shaving and valley-filling for an aggregated (residential) EV fleet is discussed. The proposed solution provides a look-ahead strategy into effective EV scheduling by co-optimizing multiple objectives. To provide operational guidance to utilities and customers, an optimal solution may be selected from those represented by the Pareto fronts.

Impact of Electric Vehicle Loads on the System Load Profile of Sri Lanka

Electric vehicles (EVs) are being promoted worldwide because of the potential they have to address atmospheric pollution issues and relieve countries from the burdens associated with the use of liquid petroleum based transport fuels. Ever increasing environmental concerns, improvements to battery technologies, entry of new manufacturers and new vehicle models, and introduction of favourable fiscal policies have all contributed to the increase in EV penetration rates worldwide including Sri Lanka. However, it is not clear how the power system in Sri Lanka would face the challenge of this new and unknown demand which would get added to the already exiting demand profile and which would be a result of the stochastic nature of the battery charging behaviour of EVs. In this research study, EV charging modes and charger types available in the market were considered and a model was established to ascertain the relationship between the charging demand and the other contributory factors such as EV battery sizes, charge remaining at the commencement of recharging, charging rates at different places of the chronological load profile, charging habits of customers and time-of-use pricing (TOU) policies. The probability distribution of variables such as the time of commencement of charging and battery charge duration was considered at a significantly acceptable level. By combining the probability distribution curves of the said variables, several EV charge demand curves were established using Monte Carlo method and the charging demand curves were subsequently superimposed on the system load profile. While the proposed methodology gives an insight into the impact of the EV load on the system load profile, it also shows how an effective control of EV charging could bring down the operational costs and investment cost of a power system.

Aircraft batteries: current trend towards more electric aircraft

Competition in the aircraft industry market and global warming has driven the industry to think along economic and environmental lines. This has resulted in the emergence of more electric aircraft (MEA). The increase in the power demand of aircraft, especially in the last two decades, coupled with advancement in battery materials and technology has led to the development of many high energy density batteries. This study presents an overview of the battery systems for MEA. In this paper, a study on the battery technologies used in aircraft in the last five decades is being done. A general background of the battery system is presented and the performance of the batteries based on energy densities and low temperature capabilities are evaluated and discussed. Evolution of MEA with its power system architecture and load profile is presented to understand the requirements of the battery system. Weight saving and cost analysis is done for the Li-ion and Ni-Cd batteries with respect to the requirement of an MEA `Aircraft X'. Battery management system (BMS) for Li-ion batteries is also explored and discussed. Based on the analysis, Li-ion battery is selected and integrated with the power distribution DC network for future MEA.

Evaluation of Well-Being Criteria in Presence of Electric Vehicles Consumption Increase and Load Shifting on Different Load Sectors

By the development and progress of electrical systems and considering the environmental issues, the use of electric vehicles has increased, which leads to changes in the system load profile as well as the peak load of the distribution network. These changes affect the reliability criteria of the power system. A method to maintain the system’s reliability criteria in an acceptable range while increasing the penetration of this type of loads could be the use of the demand side potential. By changing the rate and duration of different consumption loads, demand side management (DSM) can pave the way for further penetration of the new loads through improving the system reliability criteria and reducing the peak load. In this research, by investigating the behavior of the electric vehicles’ load and applying the potential and flexibility of different segments of the load, effects of further penetration of the electric vehicles, as the consumption load, on the system’ reliability criteria as well as effects of the response of different sectors of the load on further penetration of these loads were investigated. Simulation was performed using the Well-being Model, which divides all the system performance states in to three states of health, marginal, and risk with sequential Monte Carlo method. The simulation was conducted on the RBTS test system using MATLAB software. With regard to the results of the simulation, penetration of the electric vehicles increased the risk probability of the system; however, by applying the load shifting, the adverse effects of the presence of the electric vehicles on reliability were compensated it.

Optimal Residential Load Management in Smart Grids: A Decentralized Framework

Severe peak rebounds are likely in absence of a system-wide coordination among customers participating in demand response programs. This paper aims to establish a decentralized system-wide framework to coordinate demand response of residential customers in a smart grid. The objective of the framework is to modify system load profile provided that customers’ payments are minimized, and their comfort and privacy are preserved. Home load management (HLM) modules, embedded in customers’ smart meters are autonomous agents of the framework. The energy service provider iteratively exchanges load information with HLM modules in the hope of achieving his desired load profile. In each iteration, the service provider announces system load profile to HLM modules. The modules, keeping in mind their own financial and comfort constraints, nonsequentially send back load reschedule proposals to modify system load profile. The received proposals are judged whether they improve system load profile or not. HLM modules with accepted proposals apply their proposed schedules. The modified system load profile is then released, and HLM modules’ new proposals are gathered and judged. This procedure is repeated to the point at which no further improvement in the system load profile can be experienced. Performance of the framework is shown by applying it to a system with 50 customers.

Distribution Markets

The proliferation of proactive customers in distribution systems may be considered as one of the factors driving changes in system operation and control. These changes may require revisiting the traditional utility business models. With the proliferation of proactive customers, the adoption of distributed energy resources (DERs) is also increasing, largely due to the falling cost of the DER technologies, numerous economic incentives and a more ubiquitous utilization of demand response (DR) practices using smart appliances. These customers, on one hand, may provide benefits for the power system as they can potentially reduce system peak loads, shape the system load profile, and defer required generation and transmission upgrades. On the other hand, a large penetration of these lproactiver customers could challenge the economic and reliable operation of the power system. Considering the significant changes that are occurring in the distribution sector, utilities may need to find new solutions to address the needs of future power grids. This article addresses these needs by investigating the conceptual framework of a lDistribution System Operator (DSO)r as a novel solution that can be adopted by distribution utilities. This article further focuses on the benefits and drawbacks of implementing a DSO, roles and functionalities, and specific requirements.

A Hierarchical Framework for Coordinated Charging of Plug-In Electric Vehicles in China

Plug-in electric vehicle (PEV) technology has drawn increasing amounts of attention in the last decade. As the world’s largest automotive market, China has recently made the electrification of transportation central to its national strategic plan. Because of the unique nature of the vertically regulated power industry, China’s massive deployment of PEVs has to face unique challenges that may not be encountered by any other country/region. Therefore, a comprehensive coordinated PEV charging scheme is urgently needed to facilitate the smooth grid integration of PEVs at all levels (e.g., transmission systems, distribution systems, and charging stations). This paper presents detailed mathematical modeling of a novel hierarchical framework for coordinated PEV charging at multiple timescales (i.e., day-ahead and real-time). The proposed three-level (e.g., provincial level, municipal level, and charging station level) PEV charging strategy jointly optimizes system load profile and charging costs while satisfying customer charging requirements. The interrelationships between various levels in terms of energy transaction and information exchange are clearly identified. Case studies on Guangdong Province, China, are carried out and simulation results demonstrate the effectiveness of our proposed hierarchical control framework in reducing system peak demand and charging costs.