Peak Load Periods Research Articles

A Time-phased Order Charging Strategy for an Electric Vehicle Photovoltaic Charging Station

Introduction Aiming at the problem of orderly charging of electric vehicle photovoltaic charging stations， the paper aims to establish a bucketed order charging strategy. Method The structure and working principle of the electric vehicle photovoltaic charging station were studied， and the charging conditions of the electric vehicle and the state of charge of the energy storage battery were constructed. During the peak load period， while meeting the vehicle charging demand， reduces the power grid purchases and the cost of electricity purchase， assisted the power grid to “eliminate the peak”. During the low load period， while meeting the vehicle charging demand， increased grid purchases and assists the power grid to “fill the valley”. Result The validity of the model is verified by simulation examples， compared with instant charging solutions， shows the advantages of the builted model in reducing the cost of electricity purchase and reducing the peak-to-valley difference of the power grid. Conclusion The charging strategy of the proposed electric vehicle photovoltaic charging station is correct and effective， and can provide reference for practical applications.

Application of Phase Change Materials and Conventional Thermal Mass for Control of Roof-Generated Cooling Loads

Among all of the internal fabric and external enclosure components of buildings, sloped roofs and adjacent attics are often the most dynamic areas. Roofs are exposed to high temperature fluctuations and intense solar radiation that are subject to seasonal changes in climatic conditions. Following the currently rising interests in demand-side management, building energy dynamics, and the thermal response characteristics of building components, this paper contains unpublished results from past studies that focused on innovative roof and attic configurations. The authors share unique design strategies that yield significant reduction of daytime roof peak temperatures, thermal-load shavings, and up to a ten-hour shift of the peak load period. Furthermore, advance configurations of the roofs and attics that are discussed in this paper enable over 90% reductions in roof-generated peak-hour cooling loads and sometimes close to 50% reductions in overall roof-generated cooling loads as compared with traditionally constructed roofs with the same or similar levels of thermal insulation. It is expected that the proposed new roof design schemes could support the effective management of dynamic energy demand in future buildings.

Fuzzy Energy Management Scheme for a Hybrid Power Sources of High-Altitude Pseudosatellite

HAPS (high-altitude pseudosatellites) are flight machines, airplane type, generally without pilot which fly in a definite zone at 18-22 km altitude, providing communication and surveillance services. These flight machines do not leave the atmosphere, and their purpose is to maintain a constant flight level for as long time as possible in the interest zone (e.g., five years) to fulfill their mission. HAPS energetic system proposed in this paper has to feed the electric propulsion system of HAPS (12.5 kW) and also to feed on-board equipment (navigation, data links, scientific equipment, etc.). On-board energy sources have to maintain HAPS in the interest zone for long periods. For this reason, it is used in the present solar power sources. A part of the generated energy is consumed on board; the rest is stored daytime and consumed nighttime. So, the system is provided with energy generation systems and also with storage and management systems. HAPS energetic system is a hybrid type, with two or more power sources. In this case, power sources are photovoltaic panels are used daytime and fuel cell are used nighttime, and also, a battery and/or a supercapacitor is used in transition periods from day to night and in peak load periods. In this paper, an electric power system used nighttime is designed and analysed. In this situation, the primary power source is the fuel cell and the secondary power sources are battery and/or supercapacitor. There are used numerical simulations models, developed in Matlab/Simulink, for all hybrid power source components: fuel cell stack, battery system, supercapacitors, conversion system, and fuzzy logic power management system. For a part of these components, there are used existing simulation models in Matlab/Simulink, adapted to these simulation requirements, and for others, there are designed and implemented simulation schemes according to these simulation requirements. An important component of the hybrid power source is the power conversion system which adapts the power sources parameters to consumer input requirements. A fuzzy logic power management system is designed.

Increase in Speed of Regular Urban Bus Traffic

The article outlines the currently relevant directions for increasing the efficiency of passenger transportation by urban transport, achievable through using modern digital technologies for monitoring and controlling congestion of the road network, traffic management, and assessing reliability of the transportation process. The objective of the study was to develop theoretical base and practical recommendations for increasing speed of traffic on regular urban bus routes during periods of high load of the urban road network using alternate roads. Theoretical studies were based on the analysis of scientific and regulatory sources, and on the system analysis of transportation processes. Experimental studies were carried out in laboratory and road conditions using mathematical modelling, methods of mathematical statistics, technical, economic and system analysis of transportation processes, analysis of passenger flows, field observations. The dependences of the flow rate of vehicles on traffic intensity, technical speed of buses on alternate roads on the flow rate of fixed-route vehicles are obtained. The economic effect resulting from implementation of recommendations was calculated for the conditions of the city of Magnitogorsk, since redirecting the buses to alternate roads during peak load periods for road network allows to increase their route speed by 7 km/h and hence to reduce the need in bus fleet by 2 units, obtaining the target economic effect.

Optimal Sizing of a Real Remote Japanese Microgrid with Sea Water Electrolysis Plant Under Time-Based Demand Response Programs

Optimal sizing of power systems has a tremendous effective role in reducing the total system cost by preventing unneeded investment in installing unnecessary generating units. This paper presents an optimal sizing and planning strategy for a completely hybrid renewable energy power system in a remote Japanese island, which is composed of photovoltaic (PV), wind generators (WG), battery energy storage system (BESS), fuel cell (FC), seawater electrolysis plant, and hydrogen tank. Demand response programs are applied to overcome the performance variance of renewable energy systems (RESs) as they offer an efficient solution for many problems such as generation cost, high demand peak to average ratios, and assist grid reliability during peak load periods. Real-Time Pricing (RTP), which is deployed in this work, is one of the main price-based demand response groups used to regulate electricity consumption of consumers. Four case studies are considered to confirm the robustness and effectiveness of the proposed schemes. Mixed-Integer Linear Programming (MILP) is utilized to optimize the size of the system’s components to decrease the total system cost and maximize the profits at the same time.

A Novel Model for VPP to Participate in Capacity Market Auction

With the development of intermittent renewable energy, power system needs more capacity resources to ensure the generation adequacy during peak load periods. Flexible resources such as electric vehicles(EVs) and demand response resources(DRRs) will play important roles in the capacity market auction. However, due to the uncertainty of outputs, they are not competitive enough in the market and face high investment risks. In order to deal with this situation, we integrate renewable energy resources(RESs), EVs and DRRs into a virtual power plant (VPP) and proposed a novel auction model for the VPP to participate in the capacity market. The objective function is to maximize the social welfare while satisfying the reliability of the system. This paper also presents the results of a case study with three scenarios to prove the validity. The proposed model provides a novel path for distributed flexible resources to participate in capacity market.

Optimal design of reward-penalty demand response programs in smart power grids

In smart and sustainable power grids, incentive-based demand response programs (DRPs) encourage customers to shift their demand from peak load periods. Broad participation of customers in these programs may lead to a peak rebound and the distribution system congestion in lower load periods, endangering the supply of reliable and sustainable electricity to urban consumers. To tackle these issues, this paper proposes a reward-penalty DRP for customers in smart distribution networks. In the proposed program, the customers receive rewards as they reduce or shift their demands in requested hours. The customers may also be penalized if their demands exceed predefined thresholds. An optimization problem is proposed to determine the optimal penalty and reward values. The objective function is to maximize the distribution company's profit, considering operational constraints of the distribution system. The proposed program is examined on a test system, and the results are compared with the broad application of conventional incentive-based DRPs. The results indicate that the proposed reward-penalty DRP can improve the network operational characteristics and mitigate the peak rebound, without imposing more costs on customers.

Potential use of thermal energy storage for shifting cooling and heating load to off‐peak load: A case study for residential building in Canada

AbstractThis study presents an investigation of the potential use of thermal energy storage for shifting cooling and heating loads to off‐peak hours in order to balance the electricity production and demand periods. In Canada, the main energy source for cooling and heating applications is generally electricity. Therefore, cooling and heating devices have a great impact on the electricity peak load. In this study, it is considered that heating and cooling loads on the electricity peak load periods are shifted to off‐peak hours by thermal energy storage systems. At the end of this study, it is observed that the thermal energy storage has great potential for shifting electricity peak load depending on cooling and heating load to off‐peak periods. The electricity peak loads can be reduced by 25% and 45% by shifting heating and cooling loads to off‐peak hours and doing storage. Furthermore, the thermal energy storage systems can help reduce both cooling and heating costs in Canadian dwellings by 20% and 18%, respectively.

Seasonal variability of price elasticity of demand of households using zonal tariffs and its impact on hourly load of the power system

Price elasticity of electricity demand values are crucial for a proper design or retrofit of time-of-use electricity tariffs. The aim of this paper was to propose an alternative methodology for determination of self and cross price elasticity of demand with daily resolution for electricity customers changing their settlement to the time-of-use tariff. Progressing implementation of smart metering systems provides access to more accurate data reflecting the consumers’ electricity consumption with at least hourly resolution, which has been a motivation to use load profiles data to analyze customer’s price elasticity. The method is based on load profiles’ comparison of customers using flat and time-of-use tariffs. A case-study example is presented for residential consumers settled according to time-of-use tariff in the area of one of Polish distribution system operators and the resulting price elasticity values vary significantly over the year. Effectiveness of zonal tariffs is also evaluated, along with their impact on the power system’s load in the summer and winter peak load periods. The results may be used by supply companies and distribution system operators to improve efficiency of applied demand response programs based on time-of-use tariffs and thus influence the safety of the power system operation.

AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform

A Virtual Power Plant (VPP) is a network of distributed power generating units, flexible power consumers, and storage systems. A VPP balances the load on the grid by allocating the power generated by different linked units during periods of peak load. Demand-side energy equipment, such as Electric Vehicles (EVs) and mobile robots, can also balance the energy supply-demand when effectively deployed. However, fluctuation of the power generated by the various power units makes the supply power balance a challenging goal. Moreover, the communication security between a VPP aggregator and end facilities is critical and has not been carefully investigated. This paper proposes an AI-enabled, blockchain-based electric vehicle integration system, named AEBIS for power management in a smart grid platform. The system is based on an artificial neural-network and federated learning approaches for EV charge prediction, in which the EV fleet is employed as a consumer and as a supplier of electrical energy within a VPP platform. The evaluation results show that the proposed approach achieved high power consumption forecast with $\mathit {R^{2}}$ score of 0.938 in the conventional training scenario. When applying a federated learning approach, the accuracy decreased by only 1.7%. Therefore, with the accurate prediction of power consumption, the proposed system produces reliable and timely service to supply extra electricity from the vehicular network, decreasing the power fluctuation level. Also, the employment of AI-chip ensures a cost-efficient performance. Moreover, introducing blockchain technology in the system further achieves a secure and transparent service at the expense of an acceptable memory and latency cost.

Day-Ahead Optimization Scheduling for Islanded Microgrid Considering Units Frequency Regulation Characteristics and Demand Response

With the increase of distributed generation penetration, the economic operation of microgrid has been fully developed. For islanded microgrid, due to the lack of large grid support and the limited capacity of the distributed generation (DG), the energy supply of microgrid may not meet the need of users in real time. Therefore, it is of great significance to solve the problem of insufficient power supply of islanded microgrid and improve the economy of its operation. Firstly, this paper analyses the frequency characteristics of DG, and establishes power models oriented to the frequency regulation characteristics of microgrid and introduces the mathematical model of DG. Then, an islanded microgrid day-ahead optimization scheduling model considering frequency regulation characteristics of units and demand response (DR) is proposed. The frequency characteristics of controllable units and DR are considered in the model, and different strategies are selected to analyze their impact on microgrid economy. The example shows that the optimization scheduling model of microgrid established in this paper can reduce the power supply pressure during peak load periods, alleviate the problem of insufficient power supply, and effectively improve the overall benefit of microgrid, which verifies the rationality of the model.

Comparing strategies for improving thermal performance of an existing district heating (DH) network: low temperature DH in Omsk, Russia

In Russia district heating (DH) systems are characterized by the high wear-out rate of key facilities, particularly energy distribution networks and plants, the inadequate reliability of operation, significant heat losses, and the polluting impact on the environment. The objective of this paper is to indicate the feasibility and advantage of the renovation on a traditional high-temperature DH network. All the calculations are performed in Zulu©, Russian commercial software for simulation of a district energy system’s behavior. Reference operational data for the DH system was obtained from database established by the local heat supply company in the form of Microsoft Office Excel spreadsheet file. The second idea (Case-2) envisages installing a heat pump and increasing the supply temperature in peak load periods during the heating season to limit a size of a heat pump. Case-3 and -4 are related to installing in-room terminal units and operating the system ‘as is’ respectively. Terminal units include consoles, fan-coils, blower coils, furnaces, chimneys, and radiant panels. The fifth option introduces low temperature district heating (LTDH) concept representing a paradigm shift in DH development and features a low supply temperature and smart control. When designing all errors should be less than local regulations allowable for each object. Presented research revealed that the issue of modernisation in Omsk, Russia cannot be easily and clear resolved. The future state-determination of results of DH network retrofitting is innovatively proposed in this paper.

Study on the modernisation of an extra-worn district heating (DH) system in Russia: low temperature DH and 4 more options processing

Modeling was performed on the base of the DH system located in Omsk, Russia, where the DH network temperature requirements are not met and design outdoor temperature of extreme -37°C is. Surveyed investment in a transmission line to avoid penalties on disturbances is projected to have an original supply temperature of 150°C and is denoted as Case-1. The second idea (Case-2) envisages installing a heat pump and increasing the supply temperature in peak load periods during the heating season. The third option is to use of in-room terminal systems to provide heating to individual zones. Case-4 assumes maintaining an ordinary DH network without using any energy-efficient alternative and significant repair which means that the system continuous working ‘as is’. The fifth option introduces low temperature district heating (LTDH) concept featuring a low supply temperature and smart control. To sum up, this research indicates location of a heat pump and also shows how the piping system will be offset to allow the normal operation. This study presents a framework to represent, aggregate, dynamic thermal model and modernize a DH system based on a high-level equation-based simulation software and a five-option feasibility study.

Secured Energy Trading Using Byzantine-Based Blockchain Consensus

To mitigate the problems of demand-supply mismatch in the future grid the solution of renewable energy source (RES) integration results in a bidirectional flow of information and transactions, which are prone to different kinds of cyber attacks, especially in energy trading where the security of financial transactions is of most concern. Electric vehicle (EV) having the advantage of mobility can play a significant role in maintaining demand-supply balance at any location unlike their peers (conventional compensator). For deciding entire system security, securing EVs charging-discharging transactions at all charging stations or connecting points is most important. The system can be made more secure against cyber-attacks with the introduction of the blockchain framework. Hence, in view of secured transactions, the paper focuses on the energy trading process between EVs and distribution network (DN) in a Byzantine based blockchain consensus framework. During peak load period DN initiates the energy trading process by demanding additional power from the EVs. This process of energy trading results in energy and information exchange which needs to be secured through blockchain from vulnerable attacks and threats. Possible scenarios of various cyber-attacks on different nodes of the system are visualized in the form of false data. To highlight the application of blockchain, the Byzantine general problem framework is used which states that for successful attack 33% of information is to be manipulated, in other words, decreasing the probability of attack confirms the system security. Numerical results based on various operating scenarios for the standard IEEE 33 bus system are in agreement with the Byzantine consensus problem indicating improvement in system security.

The Influence of Electric Vehicle Charging on Low Voltage Grids with Characteristics Typical for Germany

The increasing number of electric vehicles poses new challenges to the power grid. Their charging process stresses the power system, as additional energy has to be supplied, especially during peak load periods. This additional load can result in critical network situations depending on various parameters. These impacts may vary based on market penetration, the energy demand, the plug-in time, the charging rate, and the grid topology and the associated operational equipment. Hence, the impact of electric vehicles (EVs) on the power grid was analysed for twelve typical German low voltage grids by applying power flow calculations. One main result was that thermal and voltage-related network overloads were highly dependent on market penetration and grid topology.

Research on Interruptible Load Management of Electric Vehicle Charging Considering Price Risk

The rapid development of electric vehicle industry promotes the construction of charging infrastructure and charging station. In the operation of charging station, how to ensure the stable profit of charging equipment under the condition of excessive charging load has become a crucial issue. Based on this, this paper focuses on how charging stations sign interruptible load contracts with users during peak load period and consider their risk coefficients to formulate optimal competition strategies so as to maximize expected profits. Finally, the problems and conclusions are summarized.

An Orderly Power Utilization Scheme Based on an Intelligent Multi-Agent Apanage Management System

Orderly power utilization (OPU) is an important measure to alleviate contradiction between supply and demand in a power system peak load period. As a load management system becomes smarter, it is necessary to fully explore the interactive ability among users and make schemes for OPU more applicable. Therefore, an intelligent multi-agent apanage management system that includes a mutual aid mechanism (MAM) is proposed. In the decision-making scheme, users’ participation patterns and the potential of peak shifting and willingness are considered, as well as the interests of both power consumers and power grid are comprehensively considered. For residential users, the charging time for their electric vehicles (EVs) is managed to consume the locally distributed power generation. To fully exploit user response potential, the algorithm for improved clustering by fast search and find of density peaks (I-CFSFDP), i.e., clusters the power load curve, is proposed. To conduct electrical mutual aid among users and adjust the schemes reasonably, a multi-objective optimization model (M2OM) is established based on the cluster load curves. The objectives include the OPU control cost, the user’s electricity cost, and the consumption of distributed photovoltaic (PV). Our results of a case study show that the above method is effective and economical for improving interactive ability among users. Agents can coordinate their apanage power resources optimally. Experiments and examples verify the practicability and effectiveness of the improved algorithm proposed in this study.

Demonstration of heating demand peak shaving in smart homes

Flexible heating demand in buildings plays an important role in achieving a carbon neutral society. For the district heating system of Copenhagen, heating demand flexibility can help to eliminate the use of fossil-fueled boilers that are used during peak-load periods. In this project, field tests in 16 apartments were conducted, aiming to gain insights into the use of information and communication technologies (ICT) to manage heating systems operation for flexible demand. The apartments are equipped with sensors and devices interfaced to an ICT system composed of blocks responsible for data storage, monitoring and control. In the experiments, we controlled temperature setpoints of individual rooms during defined periods of the day, and continuously developed control strategies throughout the tests in the heating season 2018/19. The final algorithm was implemented with features to reduce the rebound effect and include residents feedback. All the algorithms are generic and can be applied in other smart homes where heating supply to rooms is controlled using thermostats. The ICT system architecture used in the experiments showed to be a feasible way to implement demand side management (DSM) in the heating system, and the learning process of the experiments resulted in improvements on the control strategies, leading to a better system performance.

A Hybrid Multi-Objective Crisscross Optimization for Dynamic Economic/Emission Dispatch Considering Plug-In Electric Vehicles Penetration

Due to the significant uncertainty of charging time and charging power consumption, the large increase in plug-in electric vehicles (PEVs) may create a major influences on the power system: According to people’s living habits, PEVs are basically charged during peak load periods (after work). Once PEVs continue the random charging behavior, there will be a higher difference of peak-valley and bigger burden on the grid. A new strategy is put forward for dynamic economic/emission dispatch (DEED) with the consideration of PEVs for the purpose to shave the peak and fill the valley in this paper, and the influences brought from different loads of grid-to-vehicle (G2V) and vehicle-to-grid (V2G) on DEED problem are discussed. The problem to be solved is a challenging multi-objective non-linear problem. By taking advantage of the differential evolution (DE) algorithms and a newly developed crisscross optimization algorithm, a new multi-objective hybrid optimization algorithm is put forward to deal with the problem including effectively dealing with the inequality and equality constraints. A case study is presented to show the feasibility and effectiveness of the put forward method. The analysis results demonstrate that the put forward algorithm could effectively solve DEED problem, showing that the resulting approach of peak shaving and valley filling could significantly save economic costs and reduce emissions under the same load.

A sensitivity analysis on the heating and cooling energy flexibility of residential buildings

The present paper evaluates the potential of the buildings thermal inertia to modify their heat load pattern using two energy flexibility indicators: the available heat storage capacity and the efficiency of active demand response events. The study analyses the effects of building envelope, user behaviour and weather conditions on the flexibility indicators for three reference apartments. The results show that during the heating season the available storage capacity increases from new to old buildings. Part of the thermal energy shifted is lost through the envelope after the events, resulting in lower efficiencies for old buildings. In general, upwards and downwards modulation events are preferable just before and just after the peak load periods, respectively. The paper shows that severe weather conditions and intermittent set-point schedules lead to exceptions to this general rule. In the heating season, forced modulation events do not bring to significant variation of thermal comfort in well-insulated buildings, whereas they do in old ones. During the cooling season, the choice of the best event is more difficult due to the high variability of diurnal heat gains and the indoor thermal conditions depend on the start time of events rather than on the type of building envelope.