Grid Cost Research Articles

An Intelligent Electric Vehicle Charging System in a Smart Grid Using Artificial Intelligence

ABSTRACTThe rapid combination of the electric vehicles into the recent transportation prefers very efficient charging systems involved in grid conditions. This increasing adoption of electric vehicles demands a dynamic and intelligent framework to control charging, confirming optimal grid performance, load balancing, and cost efficiency. In this work, we developed an optimized deep learning framework using the combined structure of Whale‐Optimized Neuro‐Fuzzy Classification for controlling electric vehicle charging within the grid. The increasing involvement of electric vehicle's produces new difficulties, involving overload of grid, and energy management, in real‐time and optimized decision making process. The Whale‐Optimized Neuro‐Fuzzy Classification method uses the hybrid abilities of neuro‐fuzzy systems, integrates the capability of the neural networks and also optimize using a Whale Optimization Algorithm for improved accuracy and efficiency. This proposed method maintains the charging and discharging process of electric vehicles, which have several factors like grid load, priorities of the vehicle and preferences of the user. The neuro‐fuzzy system combines deep convolutional neural network and fuzzy logic to predict charging patterns, considering user preferences, grid demand, renewable energy availability, and so on. This method contribute to the enhancement of energy systems, denotes the future requirement of future smart cities. Evaluation metrics included power usage, energy loss, cost, and processing speed. Analysis of experimental results revealed the accuracy of 99% for the proposed method compared to existing techniques.

Explainable artificial intelligence of tree-based algorithms for fault detection and diagnosis in grid-connected photovoltaic systems

A grid-connected photovoltaic system integrates solar panels with the utility grid through a power inverter unit, allowing them to operate in parallel with the grid. Commonly known as grid-tied or on-grid solar systems, these configurations enable panels to feed electrical energy back into the grid, offering simplicity, low operating and maintenance costs, and reduced electricity bills. Despite these advantages, this environmentally friendly energy solution is still susceptible to downtimes and faults. This study utilizes advanced machine learning tree-based algorithms for fault detection and diagnosis in such systems with the goal of maintaining reliability, improving performance, and ensuring optimal energy generation. Specifically, the research investigates the effectiveness of Extra Trees as a fault detection and diagnosis algorithm through an efficient two-phase framework that consists of a binary fault detection phase followed by a multi-class fault diagnosis phase, achieving respective accuracies of 99.5% and 98.7%. In addition, the study underscores the importance of oversampling in improving results, particularly for imbalanced datasets. Moreover, explainable artificial intelligence is employed to enhance transparency in the model’s output and sensitivity to specific features in a given order. Remarkably, the findings align directly with results obtained from techniques such as feature importance averaging and incremental feature accuracy tracking. The research unveils a highly scalable, lightweight, and simple framework for fault detection and diagnosis in grid-connected photovoltaic systems.

Implications of Large-Scale PV Integration on Grid Operation, Costs, and Emissions: Challenges and Proposed Solutions

This study examines integrating large-scale photovoltaic (PV) systems into the power grid to achieve a 30% PV share, addressing operational and economic challenges such as backup generation, storage, and grid stability. Applying an electricity dispatch model to the test case of Israel, it highlights significant impacts on fuel consumption, cost, and carbon emissions. Key findings include an 8% drop in the capacity factor of natural gas combined cycle (NGCC) plants, leading to increased starts, stops, and higher fuel consumption. Annual power generation will grow from 81 to 104 TWh, with PV generation increasing from 8.1 to 31.1 TWh. Open cycle gas turbine (OCGT) output will grow from 2.4 to 10.2 TWh, increasing OCGT’s market share from 3% to 10%. NGCC operations’ intermittency will double annual starts from 3721 to 7793, causing a 1.1% efficiency drop and a 2% rise in natural gas consumption. 3.45 GWh of Li-ion battery capacity will be needed. The LCoE is expected to increase from 6.6 to 7.0 c$/kWh without a carbon tax and from 8.7 to 8.8 c$/kWh with a $40/t carbon tax. Annual emissions will rise from 41.8 to 46.5 Mt. This study provides insights for sunny Mediterranean countries with similar renewable energy goals.

Low-carbon scheduling strategy for electric vehicles considering carbon emission flow and dynamic electricity prices

As the global environmental pollution problem intensifies, the carbon reduction transformation of the power system is urgent. In order to solve the problem of unclear carbon flow and distribution in the operation of the power grid, as well as the mismatch between static time-of-use electricity prices and peak and valley periods in the scheduling of electric vehicle charging loads, a multiperiod dynamic electricity price guidance strategy based on carbon emission flow theory is proposed. First, based on the accurate power flow results of the power system, a complex power distribution matrix of the power system is constructed to obtain the distribution of the power generated by the generator units in each node of the network. Then, the Monte Carlo random sampling method is used to simulate the load situation of electric vehicles in a disordered charging state. A mathematical model based on the carbon trading model is established to minimize the load difference at the grid end and maximize the cost of charging on the user side. Finally, the proposed ordered charging method with a multiperiod dynamic electricity pricing strategy is compared with unordered charging, and considering the participation of electric vehicles in carbon trading, this strategy effectively reduces the peak valley difference between the power grid and user charging costs.

Optimized Smart Home Energy Management System: Reducing Grid Consumption and Costs through Real-Time Pricing and Hybrid Architecture

Utility authorities utilize various methods to promote end-user energy conservation, including higher tariff rates and demand response (DR) strategies. This paper investigates an Optimized Smart Home Energy Management System (OSHEMS) designed to minimize grid dependence and energy bills while ensuring reliable load delivery. A hybrid architecture prototype was implemented, integrating a photovoltaic (PV) array, battery storage, and the electrical grid. The system combines Maximum Power Point Tracking (MPPT) solar chargers and Pure Sine Wave (PSW) inverters for efficient energy management. The Home Energy Management Whale Optimization Algorithm (HEMWOA) was employed to optimize energy usage and achieve cost reduction while enhancing user comfort. Real-time pricing (RTP) tariffs incentivizing flexible energy consumption during peak hours were incorporated. OSHEMS manages, schedules, and monitors energy sources and appliances, determining the optimal consumption mix. Experimental results demonstrate a significant decrease in grid reliance (46.6%) and energy costs (57.7%) compared to non-scheduling scenarios. These findings highlight the potential of OSHEMS in promoting sustainable and cost-effective energy consumption in smart homes.

Digital financial inclusion, the belt and road initiative, and the Paris agreement: Impacts on energy transition grid costs

We investigate how digital financial inclusion, the Belt and Road Initiative, and the Paris Agreement influence the energy transition grid cost. We propose two new Kendall and Spearman wavelet cross-quantile correlation methods and utilize data from June 1, 2018, to July 31, 2024. Our findings indicate that digital financial inclusion, the Paris Agreement, and artificial intelligence significantly reduce grid costs in the short and long run. Additionally, the Belt and Road Initiative has substantial potential to decrease grid costs, particularly during bullish market conditions in the long run. Conversely, GCOVOL significantly increases grid costs, especially in the long run.

Collaborative water-electricity operation optimization of a photovoltaic/pumped storage-based aquaculture energy system considering water evaporation effects

The increasing global population, economic development, and urbanization trends have led to a heightened demand for seafood, presenting a significant challenge to the growth trajectory of the food industry. As the most rapidly expanding segment within the food industry, aquaculture presents a sustainable solution to mitigate the overexploitation of marine resources and address the growing demand for food. Aquaculture's sufficient aquatic expanses offer considerable potential for PV deployment, thereby relieving the demand for limited land resources. However, existing research rarely addresses the collaborative operation of water and electricity in aquaculture systems. The effects of water evaporation from PV panel-covered water surfaces on the collaborative water-electricity operation are generally neglected. Hence, this work proposes a collaborative water-electricity operation of a photovoltaic (PV)-pumped storage-based aquaculture energy system considering the water evaporation effects. This optimization method accounts for the reduced water evaporation due to PV panel shading. Water surface PV and pumped storage are integrated into the system to fulfill electricity needs, with pumped storage serving as a dual-purpose device for water and electricity supply. Environmentally sustainable water-electricity generation and aquaculture energy system requirements are established. Water surface PV electricity generation is modeled based on climate and engineering conditions, while aquaculture pond electricity requirement includes oxygenation, feeding, and water replenishment. Finally, a collaborative water-electricity operation model is introduced to optimize operation strategies for various devices. A case study on a fish-light complementation project demonstrates that this optimization method can reduce reliance on the utility grid and overall system operational costs.

Smart Integration of Renewable Energy Sources Employing Setpoint Frequency Control—An Analysis on the Grid Cost of Balancing

The increasing installation of Renewable Energy Sources (RES) presents significant challenges to the stability and reliability of power systems. This paper introduces an advanced control method to mitigate the adverse effects of intermittent generation from RES on the power system frequency stability. The proposed approach emphasizes the critical role of Battery Energy Storage Systems (BESS) and RES in enhancing the resilience of modern power networks. The Generation Export Management Schemes (GEMS) are employed to curtail the excessive export of RES, thereby contributing to improved frequency stability. This research involves a comprehensive analysis of the dynamic behavior of the network under various operational scenarios, particularly focusing on power exchanges between RES, BESS, and synchronous generation units. Furthermore, this paper focuses on the economic implications of integrating RES into the grid, with a detailed cost of balancing (COB) modelling and analysis conducted to assess the financial viability of the proposed frequency management solutions. The analysis encompasses both short-term and long-term perspectives, providing insights into the development of economically sustainable smart power networks that effectively integrate renewable energy and storage technologies while maintaining system stability.

An improved meta-heuristic method for optimal optimization of electric parking lots in distribution network

In this study, a stochastic multi-objective structure for optimization of the intelligent electric parking lots (EPLs) is implemented in the distribution network for minimizing the power losses annual costs, power purchased from the main grid, unsupplied energy of subscribers, cost of vehicles to the grid as well as minimizing the network voltage deviations considering battery degradation cost (BDC) and network load uncertainty (NLUn). In this research, the unscented transformation method (UTM) is used for NLUn modeling and this method is easily applicable and has a low computational cost. An improved meta-heuristic algorithm named improved fire hawks optimization (IFHO) is utilized for decision variables finding defined as the site and size of the EPLs in the distribution network. The conventional fire hawks optimization (FHO) algorithm is inspired by the fire hawks foraging behavior and in this research, the Taylor-based neighborhood technique (TBNT) is used to reduce the dependency and the possibility of becoming trapped in local optimal. To evaluate the proposed methodology, the simulations are implemented in three scenarios (1) EPLs optimization without BDC and NLUn based-UTM, (2) EPLs optimization with BDC and without NLUn, and (3) EPLs optimization with BDC and NLUn. The results of the third scenario considering BDC and NLUn showed that the EPLs optimization integrated with a multi-objective framework by finding the EPL's optimal size and capacity in the network via the IFHO has reduced the annual losses, voltage deviations, ENS cost, and substation cost by 21.06%, 12.15%, 70.82%, and 39.10%, respectively compared to the base distribution network. Additionally, the results demonstrated that incorporating the BDC and NLUn, the annual losses, voltage oscillations, ENS cost, grid cost, and EPLs have increased in comparison with the EPLs optimization without BDC and NLUn based-UTM. In addition, the TBNT based-IFHO superiority has been confirmed in different scenarios by achieving better values of the objectives and also obtaining the convergence process with lower convergence tolerance and higher convergence accuracy.

An Orderly Charging and Discharging Strategy of Electric Vehicles Based on Space–Time Distributed Load Forecasting

Given the widespread adoption of electric vehicles, their charging load is influenced not only by vehicle numbers but also by driving and parking behaviors. This paper proposes a method for forecasting electric vehicle charging load based on these behaviors, considering both spatial and temporal distribution. Initially, the parking generation rate model predicts parking demand, establishing the spatial and temporal distribution model for electric vehicle parking needs across various vehicle types and destinations. Subsequently, analyzing daily mileage and parking demand distributions of electric vehicles informs charging demand assessment. Using the Monte Carlo simulation method, large-scale electric vehicle behaviors in different spatial and temporal contexts—parking, driving, and charging—are simulated to predict charging load distributions. Optimization of electric vehicle charging and discharging enhances grid stability, cost management, charging efficiency, and user experience, supporting smart grid development. Furthermore, charging load forecasting examples under diverse scenarios validate the model’s feasibility and effectiveness.

Operational optimization of demand management strategy for hybrid power system based on battery capacity fading characteristics

In the hybrid power system (HPS), taking battery capacity fading characteristics and demand management strategy (DMS) into consideration, an operational optimization method is developed in this work. From the energy utilization and economic perspectives, the minimum total renewable energy curtailment rate (TCR), minimum total outsourced electricity (TOE) from the grid and minimum total operating cost (TOC) are respectively taken as the optimization objectives. The corresponding mathematical models of the proposed method are established, which feature mixed integer nonlinear programming (MINLP) ones. A case study of a solar-wind-biomass-battery HPS is adopted to verify and illustrate the application and effectiveness of this proposed method. Results show that the optimal operating schemes and the DMSs of the HPS based on battery capacity fading characteristics can be determined by minimizing the TCR, TOE and TOC. The target values of TOE and TOC of the HPS with adopting DMS are 15,995.3 kWh and 18,112.1 $, which are about 70 % these of the HPS without DMS. Furthermore, the relationships between battery capacity fading characteristics and DMS are comprehensively investigated in this operational optimization of the HPS. Finally, the effects of different battery types, outsourced electricity price changes on the operation of the HPS are analyzed and discussed. This work provides a valuable method for the operational optimization of the HPS.

New Renewable Energy for Bali Tourist Destination: Potential and Challenges

Purpose: This research aims to explore potential and challenges of new renewable energy in Bali, which is a popular international tourist destination. Research methods: The research is based on several existing sources, both from daily news and existing articles regarding several renewable energy potentials in Bali. Almost all of the data is secondary data obtained from various agencies in Bali. The data are analyzed using qualitative analysis technique. Results and discussion: Economic growth and the very rapid number of tourist visits to Bali are driving problems in the energy sector. The potential for developing new renewable energy in Bali is very large, such as sunlight, wind and geothermal resources. In this case there are several challenges such as limited land, electricity grid integration, technology and costs, permits and regulations, public awareness, and the influence of weather. Implications: Collaboration between government, private sector, and society is needed to find the right solution. There is a need for a comprehensive action plan and appropriate policy support to accelerate the transition to renewable energy in Bali as an international tourist destination.

Analysis of grid flexibility in 100% electrified urban energy community: A year-long empirical study

Achieving 'carbon neutrality' in the building sector involves high penetration of renewables, electrification of energy use, and improved flexible interactions with the grid to balance energy supply and demand. This study analyzed one year in an urban energy community powered entirely by electricity, using high levels of renewable sources. By facilitating energy sharing between residential and non-residential buildings, the community redirected surplus energy to meet demand, achieving 57.6% self-consumption, 38.4% self-sufficiency, and 126.3% energy independence rate. This achievement involved an annual electricity consumption of 107 MWh, sevenfold that of previous simulation-based studies, marking the first real-world demonstration. Using machine learning clustering with domain-based interpretation, the study unraveled complex interactions between building operations and the power grid, influenced by external factors (weather, occupant behavior), system design, and controls. Managing low-probability high-impact (LPHI) events of severe demand or production peaks, accounting for only 0.5% of the time and 1.8% of net energy annually, was crucial to grid stability and cost reduction. The study confirmed the limitations of increasing battery energy storage capacity in certain environments, highlighting the importance of demand-side management (DSM) with thermal systems. The DSM effectively handled LPHI events, reducing grid stress with an 18.3% decrease in annual electricity cost .

Optimal scheduling of the stand‐alone micro grids considering the reliability cost

AbstractIn order to prevent wastage of generated power of renewable resources, the energy storage systems can be utilized in the stand‐alone micro grids to store the excess produced power of the renewable generation units. When the generated power of the renewable resources is less than the required load, the energy storage systems can help to compensate all or part of the power shortage. In the current study, a stand‐alone micro grid including wind and tidal turbines, PV systems, batteries and fuel‐based generation units is considered to supply the required load of the micro grid. The generated power of each dispatch‐able generation units is determined in such a way as to minimize the operating cost. In the operating cost of the micro grid, the operating cost of the fuel‐based generation units and the reliability cost associated to the penalty of the curtailed loads are considered. To calculate the reliability cost of the micro grid, a comprehensive reliability evaluation of the micro grid considering the resource‐dependent failure rates for all composed components is performed. To study the effectiveness of the proposed reliability‐based scheduling approach, the numerical results associated to a stand‐alone micro grid containing wind, tidal, PV and fuel‐based generation units connected to the batteries are given.

On grid-serving grid tariff design in Local Energy Markets

Local Energy Markets (LEMs) are virtual market places that allow for energy sharing of prosumers in proximity to each other. In LEMs, participants’ offers and bids are coordinated by a pricing mechanism, which sets price signals for generation and loads. This impacts distribution grid utilization. Dynamic grid fees could be used by the distribution system operator to add price signals for market participants that are beneficial to the distribution grid operation. To develop such a grid-serving LEM design, we propose a framework for the design of alternative grid tariffs as a trade-off between cost-reflective and cost-recovering grid tariffs. We analyse three alternative grid tariff components in the context of LEMs: time-varying energy fees, critical peak pricing (CPP) and capacity fees for power not traded on the LEM. A case study on a synthetically generated grid region shows that while the alternative tariff components can reduce the aggregated peak load of the region by up to 31% with the CPP, the fees can also lead to increased line utilization. Also, while total local added value can be increased with all alternative grid tariffs, especially capacity-based grid tariffs are connected to a strong redistribution of grid costs from flexible to inflexible prosumers.

Optimal power dispatching for a grid-connected electric vehicle charging station microgrid with renewable energy, battery storage and peer-to-peer energy sharing

The paper proposes an optimization approach and a modeling framework for a PV-Grid-integrated electric vehicle charging station (EVCS) with battery storage and peer-to-peer vehicle charging strategies. The main objective of the paper is to optimize the system for reliability and profitability while minimizing operational costs. The model considers factors such as energy demand, grid stability, battery health, and charging costs to efficiently utilize resources. A notable aspect of the proposed model is the introduction of a peer-to-peer energy sharing mechanism. Prosumers are allowed to trade energy with each other, enabling efficient utilization of resources and fostering a collaborative energy ecosystem. The dynamic power demand and pricing environment facilitate fair and optimized energy exchanges. The paper outlines the development of a mathematical algorithm formulation for the proposed model and validates its effectiveness and performance using a real-world case study conducted at Nelson Mandela University in South Africa. This comprehensive approach considers daily varying weather and load conditions, to ensure a realistic analysis of system performance and economic feasibility. The simulation results demonstrate significant energy demand cost savings, with a 38.14 % reduction considering time-of-use tariffs, and a 30.5 % reduction in maximum demand through optimal control and conducts a detailed techno-economic analysis over a 20-year period. The results indicate substantial reductions in grid costs and a break-even point within 8 years, highlighting the economic advantages of the optimization model. Furthermore, the results demonstrate significant cost savings, including energy demand cost savings and time-of-use savings.

Commercial electric vehicle fleets in U.S. ancillary services markets: A stochastic analysis to inform utility rate design

For commercial fleets in the United States, traditional electricity tariffs can disincentivize vehicle-to-grid (V2G) participation and render electrification less attractive. First, we show that absent a rate redesign, opportunities for both fleets and the grid are missed. We propose a rate design modification: demand charge relaxation during off-peak (overnight) hours. This approach would enable fleets to shift additional load share to overnight hours and increase ancillary service revenues without the expectation of impacting local grid cost drivers (i.e., coincident peak load). Applied to a California case study, operational savings spanning 7.5% and 20.6% are realized via increased capacity revenues and deferred demand charges.

Low-carbon operation of smart distribution grid based on life cycle assessment and ladder-type carbon trading

A large number of EVs are commonly added to peak loads without dispatch in the smart distribution grid system, and the grid system usually suffers from excessive carbon emission problems. This paper proposes an integrated approach to control the smart distribution grid system by utilizing life cycle assessment and the market mechanism. First, the system’s carbon emissions are estimated according to life cycle assessment and carbon flow theory. Then, a bi-level optimization strategy for a smart distribution grid is presented. The upper-layer operation object is the minimum comprehensive operation cost of the smart distribution grid, which is solved using an optimization algorithm. The lower-level optimization goal is to minimize the operating cost of electric vehicles, which is solved using the CPLEX toolkit. Our simulation results demonstrate that overall operating costs have been reduced by 71.11%, while SDG’s overall carbon emissions have been reduced by 49 kg. The low-carbon growth of the smart distribution grid may be successfully promoted by appropriate trading market scheduling and efficient low-carbon dispatching.

Optimization Challenges in Vehicle-to-Grid (V2G) Systems and Artificial Intelligence Solving Methods

Vehicle-to-grid (V2G) systems play a key role in the integration of electric vehicles (EVs) into smart grids by enabling bidirectional energy flows between EVs and the grid. Optimizing V2G operations poses significant challenges due to the dynamic nature of energy demand, grid constraints, and user preferences. This paper addresses the optimization challenges in V2G systems and explores the use of artificial intelligence (AI) methods to tackle these challenges. The paper provides a comprehensive analysis of existing work on optimization in V2G systems and identifies gaps where AI-driven algorithms, machine learning, metaheuristic extensions, and agile optimization concepts can be applied. Case studies and examples demonstrate the efficacy of AI-driven algorithms in optimizing V2G operations, leading to improved grid stability, cost optimization, and user satisfaction. Furthermore, agile optimization concepts are introduced to enhance flexibility and responsiveness in V2G optimization. The paper concludes with a discussion on the challenges and future directions for integrating AI-driven methods into V2G systems, highlighting the potential for these intelligent algorithms and methods.

Multi-time scale economic regulation model of virtual power plant considering multiple uncertainties of source, load and storag

A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system.