Total Electricity Cost Research Articles

Regional electricity cooperation model for cost-effective electricity management with an emphasis on economic efficiency

Effectively reducing the total electricity cost while ensuring its overall efficiency is vital for the sustainable development of power systems. Herein, we develop a regional cooperation optimization model to enhance the existing power cost model. This is achieved by incorporating power trading scenarios and pollution considerations, leveraging the distinct characteristics of power generation costs from various sources. The developed regional cooperation optimization model simultaneously accounts for production efficiency and overall power costs, ensuring equitable distribution and compensation of benefits among cooperating entities. In addition, the key findings indicate that the interregional cooperation optimization model significantly reduces the total cost of electricity operations in China. Specifically, during the 11th five-year plan period, the total electricity cost in China exhibited a noteworthy decline of USD 50,876 million owing to multi-objective cooperative optimization, constituting 25.99% of the total electricity cost compared with the preoptimization period. This study identifies the eastern and western regions as principal contributors to the cost savings achieved by the interregional cooperative optimization model. Simultaneously, recognizing their significant contributions, it is recommended that the eastern and western regions provide additional cost compensation to other regions to ensure the long-term viability of the cooperative alliance.

Evaluating conventional and renewable energy systems for green buildings: A case study on energy efficiency and cost optimization

This study explores two primary categories of power generation: conventional methods reliant on fossil fuels and renewable energy methods. Fossil fuel-based approaches, including natural gas and heavy fuel oil, contribute to environmental concerns due to their carbon dioxide (CO2) emissions, which are responsible for the greenhouse effect and global warming. Many nations, particularly in Europe, have implemented stringent measures to restrict CO2 emissions, imposing fines on industries that exceed emission limits. In response to these concerns, this study focuses on a green building project that aims to meet its power requirements through renewable energy sources. Various renewable energy resources will be evaluated, considering the merits and drawbacks of each technology, to identify the most suitable option for the building under investigation. Additionally, a comprehensive thermodynamic analysis will be conducted, including calculations of electrical needs such as cooling load, lighting load, and equipment load. A customized power calculator will also be developed for application in similar projects. The ultimate objective of this study is to achieve complete autonomy from the electric grid by utilizing renewable energy resources, transforming the building into a green building. The results indicate that at an outside temperature of 22 °C, the total heat gain decreases from 157.3 to 39 kW, reducing the total cost of electricity from 20,000 to 5100 AED/year. Conversely, at an outside temperature of 50 °C, the total heat gain increases from 157.3 to 196 kW, raising the total cost of electricity from 20,000 to 26,000 AED/year. These findings demonstrate the significant influence of outside temperature on the building's energy requirements and costs, underscoring the importance of considering temperature variations in energy efficiency and cost optimization strategies.

Operation of coupled power-gas networks with hydrogen refueling stations, responsive demands and storage systems: A novel stochastic framework

Because of factors such as low natural gas prices, low greenhouse gas emission and high efficiency has increased the penetration of natural gas-fired generators in electric power systems. Large gas consumption of gas-fired generators has caused intense interdependence of electricity and gas systems. Most often, the operational planning of power and gas systems is conducted through a co-optimisation model in which the total cost of electricity and gas systems is minimised and the constraints of both systems are considered. On the other hand, due to environmental concerns, the usage of fuel cell vehicles (FCVs) is increasing. Hydrogen refueling stations (HRSs) are needed for refueling FCVs. This paper puts forward a stochastic model for co-optimisation of wind-integrated power and gas systems, hosting HRSs, electric storage systems (ESS's) and responsive electricity and gas demands. The effect of ESS's, responsive demands, contingencies, wind uncertainties and demand response on operation of power-gas nexus is scrutinized. An incentive-based demand response program has been used for both electricity and gas demands. The case study is the Belgian gas network connected to the a 24-bus power system. The findings indicate that responsive electric demands reduce electric system cost by 9.4 %, while responsive gas demands reduce the gas network cost and total operation cost by 1 %. The contingency analysis on the power-gas nexus shows that single line outage contingencies in power system does not result in any demand shed and does not increase the operation cost of either electricity network or gas network.

P2P power trading based on reinforcement learning for nanogrid clusters

In this paper, a novel peer-to-peer (P2P) power trading for power management of nanogrid clusters with renewable energy sources is presented. Unlike power management of smart grids in megawatt power scale, power management of nanogrids in kilowatt power scale requires individual control of electric appliances. P2P trading is characterized by complex elements such as decentralized architecture, participant behaviors, and distributed energy resources. To this end, multi-objective optimization providing Pareto-optimal solutions is applied to the power management of nanogrid clusters capable of P2P trading. The P2P power trading aims at maximizing the profit as well as minimizing the grid power consumption. However, due to the conventional rule for P2P trading and trade-off property of Pareto-optimal solution, the result of P2P trading is not optimal. To improve the performance of P2P trading in terms of total electricity cost, reinforcement learning (RL) algorithms are introduced. For RL, deep neural networks are applied to P2P power trading. The RL agent of each nanogrid cluster learns the behavior of P2P trading and then fulfills P2P trading. In the proposed RL technique, graph convolutional network is employed for analyzing graph-structured data, and bidirectional long short-term memory network is utilized for data prediction, enhancing the performance of P2P trading. It is found from simulations of power management for nanogrid clusters that the electricity cost with the proposed RL technique is the lowest among the RL algorithms considered for benchmark test. The electricity cost is reduced by 36.7%, averaged over ten nanogrid clusters, as compared to the electricity cost obtained from power management without RL algorithms.

A novel efficient energy optimization in smart urban buildings based on optimal demand side management

Increasing electrical energy consumption during peak hours leads to increased electrical energy losses and the spread of environmental pollution. For this reason, demand-side management programs have been introduced to reduce consumption during peak hours. This study proposes an efficient energy optimization in Smart Urban Buildings (SUBs) based on Improved Sine Cosine Algorithm (ISCA) that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a SUBs. The proposed system's goal is to optimize the energy of SUBs appliances in order to effectively regulate load demand, with the end result being a reduction in the peak to average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. The proposed system is evaluated by comparing it with the Grasshopper Optimization Algorithm (GOA) and unscheduled cases. Without applying an optimization algorithm, the total electricity cost, carbon emission, PAR and waiting time are equal to 1703.576 ID, 34.16664 (kW), and 413.5864s respectively for RTP. While, after applying GOA, the total electricity cost, carbon emission, PAR and waiting time are improved to 1469.72 ID, 21.17 (kW), and 355.772s respectively for RTP. While, after applying the ISCA Improves the total electricity cost, PAR, and waiting time by 1206.748 ID, 16.5648 (kW), and 268.525384s respectively. Where after applying GOA, the total electricity cost, PAR, and waiting time are improved to 13.72 %, 38.00 %, and 13.97 % respectively. And after applying proposed method, the total electricity cost, PAR, and waiting time are improved to 29.16 %, 51.51 %, and 35.07 % respectively. According to the results, the created ISCA algorithm performed better than the unscheduled case and GOA scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers. Furthermore, this study has presented a novel two-stage stochastic model based on Moth-Flame Optimization Algorithm (MFOA) for the co-optimization of energy scheduling and capacity planning for systems of energy storage that would be incorporated to grid connected smart urban buildings.

Parameter Design of a Photovoltaic Storage Battery Integrated System for Detached Houses Based on Nondominated Sorting Genetic Algorithm-II

With the deteriorating environment and excessive consumption of primary energy, solar energy has become used in buildings worldwide for renewable energy. Due to the fluctuations of solar radiation, a solar photovoltaic (PV) power system is often combined with a storage battery to improve the stability of a building’s energy supply. In addition, the real-time energy consumption pattern of the residual houses fluctuates; a larger size for a PV and battery integrated system can offer more solar energy but also bring a higher equipment cost, and a smaller size for the integrated system may achieve an energy-saving effect. The traditional methods to size a PV and battery integrated system for a detached house are based on the experience method or the traversal algorithm. However, the experience method cannot consider the real-time fluctuating energy demand of a detached house, and the traversal algorithm costs too much computation time. Therefore, this study applies Nondominated Sorting Genetic Algorithm-II (NSGA-II) to size a PV and battery integrated system by optimizing total electricity cost and usage of the grid electricity simultaneously. By setting these two indicators as objectives separately, single-objective genetic algorithms (GAs) are also deployed to find the optimal size specifications of the PV and battery integrated system. The optimal solutions from NSGA-II and single-objective GAs are mutually verified, showing the high accuracy of NSGA-II, and the rapid convergence process demonstrates the time-saving effect of all these deployed genetic algorithms. The robustness of the deployed NSGA-II to various grid electricity prices is also tested, and similar optimal solutions are obtained. Compared with the experience method, the final optimal solution from NSGA-II saves 68.3% of total electricity cost with slightly more grid electricity used. Compared with the traversal algorithm, NSGA-II saves 94% of the computation time and provides more accurate size specifications for the PV and battery integrated system. This study suggests that NSGA-II is suitable for sizing a PV and battery integrated system for a detached house.

Green hydrogen exports in New Zealand and Chile can improve electricity supply security if configured as local energy insurance

Extreme weather events, for example, prolonged droughts, are increasingly stressing electricity systems and threatening the achievement of energy transition goals. At the same time, countries such as Chile and New Zealand are developing strategies to export renewable energy through green hydrogen. This paper economically evaluates the use of future green hydrogen exports as strategic storage under the logic of energy insurance, based on the assumption that Chile and New Zealand will adopt a hydrogen economy. Our first contribution is to compare the marginal costs of re-electrified green hydrogen, for different scenarios, to the value of lost load in New Zealand and Chile. We then determine the marginal cost of energy produced from green hydrogen based on projected production, transportation, and re-electrification costs for the year 2030. Finally, we estimate the eventual penalties that exporters would incur in the case of breaking their contracts and the overall resulting system costs of using hydrogen for system security. We found that re-electrifying hydrogen can be competitive with conventional technologies at 3 USD/kg. At 1.5 USD/kg, hydrogen is more competitive than fossil fuel-based technologies, for both New Zealand and Chile. Thus, in 2030, it could make economic sense for New Zealand and Chile to use hydrogen as a strategic reserve. The system cost of the proposed insurance scheme in 2030 ranges from 0.53 to 1.76 USD/MWh, which is small compared to total electricity costs. These values can be used for power system expansion and operation planning.

Analyzing EV User Behavior in Aggregator Smart Charging with ESS and Real-Time Pricing

Amidst increasing global electricity consumption, governments prioritize energy efficiency and electric vehicle (EV) integration into energy markets. This study examines EV aggregator strategies employing smart charging methods, adjusting charging rates based on user preferences. Through simulations in Quito's distribution system, it analyzes actions' effects on aggregator costs and technical conditions, with a focus on demand response (DR) strategies, particularly in residential areas. Exploring EVs' potential as energy storage via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options, the study introduces a collaborative evaluation of dynamic pricing and peak power limiting-based DR strategies, integrating bi-directional EV and energy storage system (ESS) use. It also proposes a novel mixed-integer linear programming (MILP) model for home energy management (HEM), incorporating distributed renewable energy, V2H/V2G capabilities, two-way ESS energy trading, and diverse DR strategies. This comprehensive approach assesses the impact of EV owner preferences and ESS availability on reducing total electricity costs through case studies.

Evaluating EV User Behavior on Aggregator Smart Charging with ESS and Real-Time Pricing-Based Demand Response

With rising global electricity consumption, governments prioritize energy efficiency and the integration of electric vehicles (EVs) into energy markets. This study evaluates EV aggregator strategies using a smart charging method that modulates charging power rates based on user preferences. Simulations in Quito's distribution system assess various actions' impacts on aggregator costs and technical conditions. The study focuses on demand response (DR) strategies, particularly for residential areas, exploring EVs' potential as energy storage via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options. It introduces a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies, incorporating bi-directional EV and energy storage system (ESS) use. A novel mixed-integer linear programming (MILP) model for home energy management (HEM) integrates distributed renewable energy, V2H/V2G capabilities, and two-way ESS energy trading and diverse DR strategies. This comprehensive approach assesses the impact of EV owner preferences and ESS availability on reducing total electricity costs through case studies.

Study on Distribution Path Planning of Electric Logistics Vehicles Under Partial Charging Strategy

The current situation of energy consumption and environmental pollution is becoming more and more serious, electric vehicles have gradually become an important distribution tool for logistics companies due to their advantages of low energy consumption and low pollution, however, the problems of limited battery capacity and long charging time are still the main obstacles to their effective promotion in the field of logistics and distribution. In order to improve the utilization rate of electric logistics vehicles, the charging method of the vehicle is studied in detail, and it is proposed to use partial charging strategy to optimize the distribution path scheme of electric logistics vehicles with soft time window, and to establish an optimization model with the goal of minimizing the total cost of distribution, and at the same time, consider the constraints of the vehicle's loading, the customer's soft time window, and the battery endurance; the genetic algorithm is improved by using the idea of destruction and repair in the large neighborhood search algorithm, and the problem is solving, and verify the effectiveness of the model and algorithm using the Solomon arithmetic set; and conduct a sensitivity analysis of the distribution cost by adjusting 2 parameters, the battery capacity and charging price of the electric vehicle. The results show that the partial charging strategy can effectively reduce the cost compared with the full charging strategy in the distribution activities of electric logistics vehicles. In the example, the total cost is reduced by 7.69%, the electricity cost is reduced by 8.85%, and the total distance is shortened by 7.4%. In this case, the total cost, electricity cost and time window cost increased by 6.22%, 19.83% and 6.83% respectively. At the same time, some charging strategies can also significantly improve the power utilization rate of the vehicle battery, and reduce the idle and waste of vehicle resources and power resources. This paper proposes a distribution route optimization model of electric logistics vehicles under the partial charging strategy, which can provide beneficial enlightenment for logistics enterprises in the scheme scheduling and distribution model selection in urban distribution, and is of great significance for reducing the distribution cost of logistics enterprises.

Impact of bi-directional electric vehicle and demand response on residential distributed PV capacity planning based on TOU pricing

The widespread deployment of residential distributed photovoltaic (RDPV) remains complex and challenging due to photovoltaic output intermittency, fluctuating electricity demand, and rising electric vehicle (EV) adoption. Simultaneously, the energy storage capabilities of EVs and residential demand response (DR) offer solutions for optimizing RDPV applications. This study proposes an integrated RDPV capacity planning model by encompassing EV charging, vehicle-to-home, and flexible load DR. Five scenarios are established to reveal the impact of various factors on the optimal photovoltaic installation capacity, electricity cost, self-consumption and self-sufficiency rate. A case study of three typical residential electricity demand patterns indicates that DR and vehicle-to-home significantly reduce the optimal photovoltaic installation capacity and total electricity cost. When the feed-in tariff during photovoltaic generation periods is higher than the off-peak pricing, DR results in a reduction in photovoltaic self-sufficiency rate and an increase in photovoltaic self-consumption rate. EV charging and vehicle-to-home have minimal impact on photovoltaic self-consumption rate, while EV charging significantly decreases self-sufficiency rate and vehicle-to-home exacerbates this effect.

Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources

<span lang="EN-US">This paper suggests a modified version of the former economic load dispatch (MELD) problem with the integration of wind power plant (WPP) and solar power plants (SPP) into thermal units (TUs). The target of the whole study is to cut the total producing electricity cost (TPEC) as much as possible. Three meta-heuristic algorithms, including particle swarm optimization (PSO), jellyfish search (JS) and salp swarm algorithm (SSA), are applied to solve the MELD. The real performance of these optimization tools is tested on the first system with six thermal units considering prohibited zones, and the second system with the combination of the first system and one solar, and two WPPs. In addition, the variation of load demand in 24 hours per day is also taken into account in the second system. JS is proved to be the most effective method for dealing with MELD. Furthermore, JS can also reach lower or the same TPEC as other previous algorithms. Hence, JS is a recommended to be a strong computing method for dealing with the MELD problem. </span>

Characteristics of water supply network zoning

Relevance. The original studies of water supply network and network exploitation data, as well as international experience, applied in similar conditions, and the data of existing literature, data of works on water supply zoning, which were carried out in the networks of various settlements in the Republic of Armenia, have been improved and are now being implemented effectively through the clear procedure developed by us. In our case, zoning is carried out under the conditions of the existing system, therefore the methods developed during design and reconstruction work consider the features of the existing system.Aim. To develop a technique to enhance the efficiency of water supply systems by implementing network zoning. It involves transforming the network into hydraulically separated zones isolated from each other. Within each zone, water supply can be regulated based on consumption requirements. Objects. The article discusses issues related to design and reconstruction procedures of existing water supply network zones. It defines the priority of zoning implementation for the current stage and proposes a method for assessing the permissible level of losses, taking into account the technical condition of the water supply network. Methods. The calculations for this study were performed using well-known hydraulic principles and laws. Experimental studies were conducted directly on the water supply network under production conditions. Results. The actual implementation of these measures in various urban locations revealed that reduction in water usage in a service zone was only 8–10%. However, analysis of the functioning of the reconstructed zones led to the conclusion that the already constructed zones now offer suitable conditions for productive work on leak detection and elimination. In the given example, during the operation of the study zone, 4300 m (about 25%) of the existing 17400 m pipelines in the reconstruction zone were decommissioned, along with 13 of 22 pumping stations. Over two years of work, the cost of electricity at the existing yard pumping stations was reduced by more than six times, the total cost of electricity in the zone was reduced by more than twelve times, and the amount of water entering the zone was reduced by 44% as a result of the water supply system reconstruction presented in the example.

A study of home energy management considering carbon quota

The household energy management system (HEMS) has become an important system for energy conservation and emission reduction. In this study, home energy management considering carbon quota has been established. Firstly, the household photovoltaic output model, load model of various electrical appliances, battery load model, and charging and discharging of electric vehicles (EVs) model are established. Secondly, the carbon emission and carbon quota of household appliances and EVs are considered in these models. Thirdly, the energy optimization model of minimum the household user’s total comprehensive operation cost with the minimum total electricity consumption, carbon trading cost, battery degradation cost, and carbon quota income are proposed, taking into account constraints such as the comfort of users’ energy use time. Subsequently, the improved particle swarm optimization (IPSO) algorithm is used to tackle the problem. Compared to the standard particle swarm optimization (PSO), the IPSO has significantly improved the optimization effect. By comparing the optimization results in different scenarios, the effectiveness of the strategy is verified, and the influence of different carbon trading prices on optimal energy scheduling has been analyzed. The result shows that the comprehensive consideration of carbon trading cost and total electricity cost can reduce the household carbon emissions and the total electricity cost of the household user. By increasing the carbon trading price, the user’s carbon trading income and the EV carbon quota income increase, and the overall operating cost decreases; the guidance and regulation of carbon trading price can make a valuable contribution to HEMS optimization. Compared to the original situation, the household carbon emissions are reduced by 14.58 kg, a decrease of over 21.47%, while the total comprehensive operation cost are reduced by 14.12%. Carbon quota trading can guide household users to use electricity reasonably, reducing household carbon emissions and the total cost of household electricity.

A self-learning multi-population evolutionary algorithm for flexible job shop scheduling under time-of-use pricing

Due to the energy crisis and environmental downgrade, manufacturing companies face rising costs. The pressure on manufacturers to reduce costs and increase efficiency is increasing. The flexible job shop scheduling problem considering time-of-use pricing (FJSPTOUP) is studied to minimize the maximum completion time and total electricity cost. A new self-learning multi-population evolutionary algorithm (SLMPEA) is proposed to address the static scheduling problem. A self-learning module is designed to acquire knowledge from elite individuals in subpopulations. The knowledge discovered can guide the evolution of subpopulations. To improve the convergence of the algorithm and maintain the diversity of the population, we present an elite retention and enhancement strategy. Two enhancement operators are developed to elevate the quality of elite individuals, and a selection strategy is designed for the enhancement operators. A time-of-use-based adjustment strategy is proposed to reduce the total electricity costs without increasing the maximum completion time. Experimental results show that the proposed SLMPEA outperforms the comparative algorithms in solving FJSPTOUP.

Electrification and residential well-being in China

In the past 20 years, China has undergone a rapid process of electrification, which has contributed to the modernization of people's lives. This paper associated household electricity consumption, appliance service function, total cost of electricity consumption to life well-being for the first time, and explained the structural characteristics of these changes. The welfare index from appliance services is defined as the result of the interaction between electricity consumption and appliance operation, and is used as an indicator to assess the improvement of household well-being. We sorted out 9 types of service functions of appliances, estimated the structure of household electricity consumption, total electricity cost and welfares received, and analyzed the factors that enhance welfare and their contributions. The results show that the electricity consumed by 12 main household appliances per household per year increased from 358 kWh in 2000 to 1419.5 kWh in 2020, and the total household electricity cost increases from 1315.74 yuan (RMB) to 6772.68 yuan. The quantity and structure of both have changed greatly. The service functions provided by appliances are increasingly diversified, have expanded from the basic services such as lighting, watching TV, refrigerated food to nine aspects including communication and transportation. The quality of life of residents continues to be improved, and the welfare index has increased from 2.48 to 7.82. There is a good linear relationship between the index and the three independent variables. The unit contributions of household disposable income, total electricity cost and household population size to the welfare index were 0.0836, 0.1195 and −1.6161, respectively. To accelerate electrification and enhance people's well-being, China needs to further improve the power and communication infrastructure, promote the application of intelligent technology, and implement precise encouragement policies.

An Energy-Efficient Unrelated Parallel Machine Scheduling Problem with Batch Processing and Time-of-Use Electricity Prices

The extensive consumption of energy in manufacturing has led to a large amount of greenhouse gas emissions that have caused an enormous effect on the environment. Therefore, investigating how to reduce energy consumption in manufacturing is of great significance to cleaner production. This paper considers an energy-conscious unrelated parallel batch processing machine scheduling problem under time-of-use (TOU) electricity prices. Under TOU, electricity prices vary for different periods of a day. This problem is grouping jobs into batches, assigning the batches to machines and allocating time to the batches so as to minimize the total electricity cost. A mixed-integer linear programming model and two groups of heuristics are proposed to solve this problem. The first group of heuristics first forms batches, assigns the batches to machines and finally allocates time to the batches, while the second group of heuristics first assigns jobs to machines, batches the jobs on each machine and finally allocates time to each batch. The computational results show that the SPT-FBLPT-P1 heuristic in the second group can provide high-quality solutions for large-scaled instances in a short time, in which the jobs are assigned to the machines based on the shortest processing time rule, the jobs on each machine are batched following the full-batch longest processing time algorithm, and the time is allocated to each batch following an integer programming approach. The MDEC-FBLPT-P1 heuristic that uses the minimum difference of the power consumption algorithm to assign the jobs also performed well.

Energy-Efficient Flexible Flow Shop Scheduling Under Time-Of-Use Rates with Renewable Energy Sources

In response to climate change, industries strive to curtail energy consumption while maintaining production efficiency. Focused on time-dependent electricity prices, this paper addresses the scheduling challenge in a flexible flow shop machine environment. The goal is to minimize both makespan and total electricity costs in a grid-connected manufacturing setup with battery storage, solar power, and a strategic turn on/turn of machine approach. A multi-objective mixed-integer linear programming model is developed, employing the ϵ-constraint method to reveal the trade-of between objectives and present Pareto-front solutions. The ensuing experimental study allows for the validation of the model and identification of its limits.

Temperature Control Strategy to Mitigate Electrical Energy Cost for Air Conditioning

The purpose of this paper is to developed an innovative strategy to support small consumer on reducing the electrical energy cost (EEC), define total electrical energy cost (TEEC) and compute the benefit of the pre-cooling model (BPM). The suggested method enables small consumers to manage the peak electricity demand for air conditioning (AC) autonomously and proactively. In this research, a pre-cooling model (PM) has been applied to anticipate an unexpected level of the electricity price. The main contribution of this research is to develop a PM to avoid an extreme electricity price and examines the EEC, TEEC and compute the BPM during hot days, peak season and considering to the probability spike (Pr). The finding results of research illustrated that the EEC for AC can be reduced from IDR 7539 to IDR 5199 and from IDR 13310 to IDR 9394 for hot days and peak season cases, respectively. The TEEC can be examines to be IDR 5200 and IDR 8888 when the Pr may occur during hot days and peak season, respectively. When applied the proposed method that the consumer and aggregator can earn collective benefit, such as: IDR 2340 (31.03 %) and IDR 3916 (29.42 %) if considering to hot days and peak season respectively; as well as IDR 2159 (28.63 %) and IDR 4422 (33.22 %) if considering to Pr that may occur in the midday and peak season, respectively. Consequently, it can be concluded that the suggested model is effective way to keep comfortable of room temperature, controlling electricity peak demand and reduce electricity cost for the AC. This model is evaluated based on the data from the South Sulawesi electricity market price and demand curves provided by the Indonesian State Electricity Company (referred to as PLN), as well as the value of outside temperature (Tout) considering to characteristic of building in the Makassar region.

A Case Study of the Use of Smart EV Charging for Peak Shaving in Local Area Grids

Electricity storage systems, whether electric vehicles or stationary battery storage systems, stabilize the electricity supply grid with their flexibility and thus drive the energy transition forward. Grid peak power demand has a high impact on the energy bill for commercial electricity consumers. Using battery storage capacities (EVs or stationary battery systems) can help to reduce these peaks, applying peak shaving. This study aims to address the potential of peak shaving using a PV plant and smart unidirectional and bidirectional charging technology for two fleets of electric vehicles and two comparable configurations of stationary battery storage systems on the university campus of Saarland University in Saarbrücken as a case study. Based on an annual measurement of the grid demand power of all consumers on the campus, a simulation study was carried out to compare the peak shaving potential of seven scenarios. For the sake of simplicity, it was assumed that the vehicles are connected to the charging station during working hours and can be charged and discharged within a user-defined range of state of charge. Furthermore, only the electricity costs were included in the profitability analysis; investment and operating costs were not taken into account. Compared to a reference system without battery storage capacities and a PV plant, the overall result is that the peak-shaving potential and the associated reduction in total electricity costs increases with the exclusive use of a PV system (3.2%) via the inclusion of the EV fleet (up to 3.0% for unidirectional smart charging and 8.1% for bidirectional charging) up to a stationary battery storage system (13.3%).