Real-time Electricity Research Articles

Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response

Industrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and demand response benefit. The upper level objective is the optimization of topology in the grid structure to optimize the power loss and dynamic reconstruction costs of the grid. The lower level is the energy cost considering demand response, real time price and dynamic demand response price. Firstly, the mathematical models of stable load, impact load and the steel production line load are built. The key parameters are identified by the Back Propagation neural network algorithm according to the actual production data. Secondly, considering the constraints of grid structure and load operation capacity, the impact of dynamic grid loss and real-time dynamic electricity price on the energy efficiency of the whole grid are analyzed in depth. The optimal operation model considering the dynamic reconfiguration and grid tramission loss of distribution network is built. Taking a steel plant park in Northeast China as an example, it is proved that the optimization model can improve energy efficiency on the load side by optimizing energy consumption and demand response participation time on load side. The energy cost is reduced by 17.77% on the load side, the network loss is reduced by 1.8%, and the operating cost of the power grid is reduced by 26.2%, which has a positive effect on improving energy utilization efficiency, reducing distribution network loss, and improving overall economic efficiency.

Lagrange Multiplier-Based Optimization for Hybrid Energy Management System with Renewable Energy Sources and Electric Vehicles

The issues of energy scarcity and environmental harm have become major priorities for both business and human progress. Hence, it is important and useful to focus on renewable energy research and efficient utilization of distributed energy sources (DERs). A microgrid (MG) is a self-managed system that encompasses these energy resources as well as interconnected consumers. It has the flexibility to function in both isolated and grid-connected configurations. This study aims to design an effective method of power management for a MG in the two operating modes. The proposed optimization model seeks to strike a balance between energy usage, protecting the life of batteries, and maximizing economic benefits for users in the MG, with consideration of the real-time electricity price and constraints of the power grid. Furthermore, in order to accurately account for the dynamic nature of not only the stationary battery banks used as the energy storage systems (ESS) but also the built-in batteries of electric vehicles (EVs), the model is presented as a multi-objective, multiparametric and constrained problem. The solution is proposed to be found using the Lagrange multiplier theory, which helps to achieve good performance with less computational burden. Lastly, simulation results from both the isolated and grid-connected modes also demonstrate the effectiveness of the designed method.

Topology-Aware Graph Neural Networks for Learning Feasible and Adaptive AC-OPF Solutions

Solving the optimal power flow (OPF) problem is a fundamental task to ensure the system efficiency and reliability in real-time electricity grid operations. We develop a new topology-informed graph neural network (GNN) approach for predicting the optimal solutions of real-time ac-OPF problem. To incorporate grid topology to the NN model, the proposed GNN-for-OPF framework innovatively exploits the locality property of locational marginal prices and voltage magnitude. Furthermore, we develop a physics-aware (ac-)flow feasibility regularization approach for general OPF learning. The advantages of our proposed designs include reduced model complexity, improved generalizability and feasibility guarantees. By providing the analytical understanding on the graph subspace stability under grid topology contingency, we show the proposed GNN can quickly adapt to varying grid topology by an efficient re-training strategy. Numerical tests on various test systems of different sizes have validated the prediction accuracy, improved flow feasibility, and topology adaptivity capability of our proposed GNN-based learning framework.

Low-carbon demand response strategy of buildings considering load rebound

With the carbon emission ratio of buildings increasing, including buildings in the carbon market, an inevitable trend of social development is created. Buildings are characterized by their strong seasonality, high simultaneity rate, and large load, which is not only the main factor leading to the peak load of a power grid but also an important resource for load control. Under the guidance of dynamic carbon emission prices and real-time electricity prices, it is worth considering how best to balance the peak response ability of buildings and the cause of the secondary peak leading to load rebound. Therefore, this paper proposes a low-carbon demand response strategy for buildings considering load rebound. First, a building load is classified based on how schedulable the load remains, and a building demand response model which considers how the load schedulability is constructed. Second, according to the different rebounds of different building loads, a load rebound optimization model based on linear attenuation was built, and the rebound parameters of various loads were designed. Then, the real-time carbon emission factor, load fluctuation reward, punishment factor, and real-time electricity price are introduced. Thus, the lowest running cost of the building load is taken as the objective function needed to construct a building demand response optimization model while also considering load rebound. Finally, an example analysis is used to verify the effectiveness of the proposed model in reducing the comprehensive operation cost of buildings. The proposed model not only effectively reduces the comprehensive operation cost of buildings, but also reduces carbon emissions and load fluctuations under the premise of satisfying the actual building load, which provides a new idea for the research of a low-carbon demand response strategy of buildings with load rebound.

Techno-economic analysis of residential rooftop photovoltaics in Spain

In response to the European Commission's renewable energy targets for 2030, this study presents a comprehensive, data-driven evaluation of the potential for electricity self-consumption in the Spanish residential sector based on rooftop PV systems. Utilizing real-time hourly electricity demand data and various surplus compensation policies, the research highlights the significance of data granularity, indicating that annual data-based PV estimations can lead to rooftop PV self-consumption capacity overestimations when compared to hourly data assessments. Furthermore, geographical variations reveal distinct rooftop PV self-consumption capacities between urban and rural areas, driven mainly by the prevalent building typologies. This discrepancy suggests that while rural regions, with their predominance of single-family dwellings, offer higher PV generation potential due to more available rooftop space, urban areas, dominated by multi-story buildings, face significant constraints in rooftop surface availability. Economically, the current surplus compensation policy in Spain reduces the profitability of PV installations, underscoring the necessity for enhanced policies to fully utilize all the available rooftop areas in the residential sector. The study's findings, particularly the revelation that residential rooftops in Spain may not be enough to meet the current electricity demand, emphasize the need for adaptive, region-specific policies and the potential role of energy communities. Policymakers and industry stakeholders are urged to prioritize rooftop utilization, ensuring the deployment of PV systems is both promoted and economically viable, steering Spain closer to its renewable energy and sustainability aspirations.

Risk-based optimization for facilitating the leasing services of shared energy storage among renewable energy stations

Due to the inherent power output correlation and uncertainty, renewable energy stations normally incur the deviation penalty in the day-ahead and real-time electricity market. Meanwhile, shared energy storage operators have been appearing to provide energy storage leasing services for neighboring renewable energy stations. In this context, this paper presents a novel optimization strategy to provide leasing services for renewable energy station clusters while improving the utilization rate and revenue of shared energy storage simultaneously. Especially, the proposed strategy utilizes a two-stage optimization model to incorporate the overselling risk. In the first stage, a matching index is defined to select a cluster of wind and solar power stations in the geographically-close region, when a set of highly complementary stations are selected by matching the typical output curve of the shared energy storage. In the second stage, an optimization strategy is determined to explore the benefit and risk of overselling for shared energy storage with the goal of maximizing the total revenue, when the correlation of wind and solar power output is realized in the scenario generation and sampling process. The results of numerical experiments have demonstrated that employing a moderate overselling method can provide an economical and efficient operational solution to improving the utilization of shared energy storage.

Anti-interference Technology of Multi-type Data Acquisition and Transmission Based on HPLC

With the development of the social economy, electricity consumption increases rapidly. A large amount of real-time electricity consumption data plays an important role in analyzing the power grid business. Monitoring power consumption performance through electricity consumption data is also an important indicator to improve power grid performance. By analyzing the characteristics of HPLC channels, such as capacitance characteristics, frequency loss characteristics, signal-to-noise ratio characteristics, line icing, and noise interference, the critical values of data transmission distortion caused by the characteristics of each channel were summarized. On this basis, it is concluded that suitable filters and amplifiers should be selected to improve the signal-to-noise ratio and the transmission quality of HPLC signals. Therefore, an anti-interference technology for data transmission based on HPLC is proposed. Finally, the application advantages and existing problems of HPLC technology in three-stream integration data acquisition and transmission are further explored.

The use of GIS in renewable energy specialist’s learning

In recent years, the global trend has been to gradually shift from traditional energy sources to renewable ones, with the aim of improving the environment and preventing climate change. Geographic Information Systems (GIS) are technologies that enhance the efficiency of these renewable energy resources (RER). Modern GIS provides a wide range of functionalities for the entire decision-making process. For instance, in solar power generation, GIS can address challenges such as remote monitoring of sites designated for solar power plants and evaluating their suitability. It also enables effective management of information on station operations, real-time electricity production and distribution, and planning and management of energy production resources. As GIS continues to evolve, scientists and energy professionals are discovering more applications in the RER field. However, it’s crucial to focus on training highly skilled specialists in RER to ensure GIS becomes a truly effective management tool in their professional activities. Research indicates that most student training programs in RER worldwide do not sufficiently cover GIS studies, particularly open-source software. To address this gap, the authors have developed a targeted plan for incorporating GIS into RER specialist training. The goal of this course is to familiarize future engineers with the theoretical, methodological, and technological foundations of GIS creation and operation. It also aims to help students understand the general principles of operation and acquire practical skills in using GIS to solve applied problems. The authors emphasize the importance of laboratory work, particularly using SagaGIS, to develop skills in working with raster and vector images, geospatial data analysis and decision-making, working with cartographic models and remote sensing data, and applying geoinformation methods to create structural, parametric, and thematic digital maps. The authors suggest using open GIS in training to facilitate a quality understanding for future RER specialists on the processes of creating, operating, and using GIS for real-world applications.

Research on design strategies and sensing applications of energy storage system based on renewable methanol fuel

The rapid depletion of traditional non-renewable fossil fuels has increased energy sustainability threats. Methanol is recognized as one of the viable alternatives to conventional fossil fuels mainly due to its high energy density and abundant raw materials. Direct methanol fuel cells (DMFCs) can convert the chemical energy of methanol into electricity in real time, which is one of the most effective ways to utilize methanol energy. However, DMFCs have undesirable performance, such as low fuel utilization, low cell voltage, and unstable output voltage, hindering the development of methanol energy technology. Herein, we design an energy storage system with high methanol energy efficiency based on passive micro DMFCs. This system with low power consumption (only uW scale) can extract the high chemical energy in methanol, efficiently convert it into electric energy, and store it to output a stable voltage for load. Moreover, application research on this system has been carried out. A self-powered methanol concentration sensor is designed and implemented on a printed circuit board. It can detect methanol solution with a concentration of 3 M–12 M, only requiring it to be tested as fuel without an additional power supply. The sensor has about 1.1 mW total power consumption and 0.34 s response time. This study on the energy conversion and storage of sustainable methanol fuel aims to provide a reference to explore the application of renewable energy.

Optimal reinforcement learning control of a district cooling system based on compound secondary sampling under real-time electricity prices

为推动电力系统实现碳中和，我国近年来风光等新能源发电装机规模不断增加。由于风光等新能源具有强间歇性和高度不确定性，电力系统供需实时平衡的难度不断增大。通过分时电价或实时电价政策，引导需求侧灵活负荷与新能源发电协同（即需求响应）是未来保证高比例新能源有效消纳的重要技术路径。空调负荷是城市用电负荷占比最高的单一类型负荷之一（在我国部分城市夏季高峰负荷中占比超过50%），且可利用建筑热惯性提供较好的调控能力，是近年来需求响应领域研究的热点。针对未来电力市场实时电价机制场景，本文研究大型区域供冷系统中的冰蓄冷需求响应优化控制技术。提出了基于复合两端采样机制的强化学习方法，实现对冰蓄冷系统的优化调度与控制，可以有效应对来自用户用冷需求和市场实时电价的双重不确定性。首先，针对实时电价下区域供冷系统运行控制问题的特性，构建马尔可夫决策过程。其次，采用非模型的强化学习算法对马尔科夫决策过程进行求解，并针对传统强化学习算法中均匀随机采样导致的学习效率低下的问题，利用结合立即回报和时序误差的复合两端采样机制，提高控制器的训练效率和收敛性能。最后，基于真实系统仿真模型开展实验，验证了本文所提需求响应优化控制方法的有效性。

P2P trading mode for real-time coupled electricity and carbon markets based on a new indicator green energy

The carbon trading market has introduced new opportunities for the low-carbon transformation of China's power industry. To realize real-time coupling of the electric–carbon market at the mechanism level, this study constructs a P2P trading mode that improves economic and environmental benefits through innovative mechanisms. First, we propose a novel indicator, Green Energy, which has multidimensional composite attributes and accurately characterizes the supply of clean and low-carbon energy. Green Energy is used as a matchmaking indicator in the P2P electricity market to increase the rate of renewable energy consumption and encourage emission reduction of fossil energy suppliers. Second, the carbon price is dynamically adjusted according to real-time electricity consumption information and it is transmitted to the electricity market through Green Energy. Third, blockchain cross-chain interoperability technology is introduced to establish a real-time data-sharing network for electricity and carbon co-trading. Moreover, a case study is conducted, and the simulation results show that the proposed method improves social welfare by over 18% compared to the traditional trading mode. And it can improve renewable energy utilization efficiency by 26.78% and reduce carbon emissions by more than 17%. This study provides a reference for the real-time coupled electricity and carbon markets.

Joint energy-frequency regulation electricity market design for the transition towards a renewable-rich power system

Striving to develop renewable energy generation (REG) has become a global trend. Due to high carbon emissions, fossil-fueled generation units (FFGUs) in an electrical energy market such as the day-ahead and real-time electricity markets are facing unprecedented constraints. From the perspective of FFGUs, an effective way is to change their market roles from energy suppliers with high environmental costs to secure and reliable frequency regulation service providers. To help FFGUs successfully complete the transition, a bi-level optimization model for the joint energy and regulation service market is proposed in this paper. Different from existing joint markets, both the environmental cost of FFGUs and the frequency regulation service (FRS) cost of REG units are considered in this research for market operators to determine the scheduling priorities of submitted bids from generation units. In the frequency regulation service market model, a dynamic approach to determining the FRS demand is applied, which not only incentivizes REG units to initiatively manage their outputs, but also derives the price signals through the equilibrium of the FRS demand and supply to ensure the revenue of FFGUs in the FRS market. Meanwhile, the decision-making model of the FFGUs under the proposed market mechanism is also presented. Finally, extensive numerical experiments demonstrate the feasibility and efficiency of the mechanism. Simulation results show that the proposed mechanism can stimulate a smooth transition of FFGUs by adapting to actual situations and policy changes.

A deep reinforcement learning approach for power management of battery-assisted fast-charging EV hubs participating in day-ahead and real-time electricity markets

Publicly available electric vehicle charging hubs are expected to grow, to meet the increasing charging demand of EVs. A dominant class of these will be fast-charging hubs where the EVs will arrive for charging at all hours of the day, get the requested charge, and leave promptly. The profitability of these fast-charging hubs will be highly dependent on the variation of the day-ahead prices of electricity, volatility of the real-time power market, and the randomness of EV charging demand. The hubs can hedge against these uncertainties by committing power purchases in the day-ahead electricity market and by adopting dynamic real-time power management strategies. We develop a novel two-step methodology. The first step entails a mixed integer linear program (MILP) that assists the hubs in their day-ahead power commitment. The second step employs a Markov decision process (MDP) model that derives the real-time power management control actions. The MILP is solved using a commercial solver and the MDP is solved using a deep reinforcement learning algorithm. We demonstrate the effectiveness of our methodology for a fast-charging hub, housing 150 charging stations and a battery storage system, that operates in the Pennsylvania-New Jersey- Maryland interconnection (PJM) power grid.

A novel real-time pricing for optimal DRP, considering price elasticity, and charging control methods of PHEV integrated with smart grids, using GMO algorithm

With the development of smart grids, optimizing the performance of response programs in the distribution system in operation and development, held special importance. This paper proposes an optimized method for demand response management in smart grids using real-time pricing. The method involves defining an acceptable consumption pattern and energy price through consumer-utility interaction. For more evaluation, six scenarios are defined that considered different states of energy pricing with and without the Plug-In Electric Vehicles (PHEVs) The first step establishes the demand response with real-time pricing, determining hourly load and energy prices. Price elasticity of demand is employed to gauge consumer load response to energy prices. The second step utilizes the Geometric Mean Optimizer (GMO) to optimize Demand Response Programs (DRPs) for consumers with smart programmable meters. The proposed method is tested on the IEEE-69 bus distribution network under various scenarios, demonstrating its effectiveness under 48 h. Additionally, the study investigates the impact of PHEVs and their controlled charging using the integrated three-tariff method and real-time electricity pricing. The results of the GMO algorithm are also compared with other methods to validate its advantages. The results indicate enhanced voltage profiles and reduced reactive power consumption during peak hours about 4.5% and 23%, respectively. in addition, the load factor by applying the proposed method is increased by about 18%. Moreover, the proposed method by combining the three-tariff method enables the transformer to supply approximately 60% of EVs without overloading, which is 40% more than that case without the three-tariff method.

Optimization model for home energy management system of rural dwellings

In the context of the increasing popularity of distributed energy, this paper proposes a home energy management system method considering the working state of flexible load to deal with the problem of mismatch between home energy supply and demand. Specifically, in this paper, the flexible load in the system is studied, and the model of flexible load and its working state are established. On this basis, combined with the real-time electricity price of the grid, the model of home energy management system is constructed to minimize the electricity purchase cost and improve the utilization rate of renewable energy, the genetic algorithm is used to solve it, and the influence of time step, flexible load ratio and whether to use energy storage equipment on the optimization degree of the model are analyzed. The research results show that, while meeting the requirements of human comfort and electricity demand, the daily electricity purchase cost of the system with flexible load scheduling is 22.5% lower than that of the system without flexible load scheduling, and the utilization rate of renewable energy is increased by 16.5%. In terms of model optimization, shortening the prediction time step, increasing the proportion of flexible load and using energy storage equipment can further reduce the electricity purchase cost. The case study confirms the effectiveness of the proposed home energy management system model, which can provide an efficient optimal scheduling scheme for household energy saving.

Electric Vehicle Trip Information Inference Based on Time-Series Residential Electricity Consumption

Pure electric vehicles (EVs) become more and more popular in current automotive markets. With wide application of EVs in the market, more EVs will be charged in residential homes. An advanced metering infrastructure measures real-time electricity consumption of individual residential homes. However, the advanced metering infrastructure is exposed to attacks given the fact that its components (e.g., smart meters) are located at public places. Under this situation, electricity consumption information of an individual residential home with an EV can be easily stolen. In this paper, we propose an EV Trip Information Inference System (TIIS) to infer EV trip information (origin-destination of each driving trip in a day) of a residential home based on its real-time electricity consumption measurements in a day. To the best of our knowledge, this is the first driving privacy relevant work caused only by residential electricity consumption data. For a residential home with an EV, TIIS derives the relationship between trip numbers in a day and charging-starting time, and the relationship between total driving distance and total charging energy. To derive the EV trip information of a residential home, TIIS firstly extracts an EV charging profile from its electricity consumption data and identifies EV model type and trip numbers as well as total driving distance based on EV charging profiles. Then, TIIS infers EV trip information based on the identified information. We used electricity consumption data and EV driving data to evaluate inference performance of TIIS. The experimental results demonstrate TIIS has as high as 81% trip inference accuracy and works on residential homes with different EV model types.

IoT-Based EV Charging & Its Energy Consumption Monitoring System

Abstract: We are currently facing problems with a lack of fuel. That's why we're moving to electric cars. However, people were never ready to love electric cars more than they do now. This is because it costs and there is no place to pay. Although several charging stations are open to the public, it still takes more time to charge a car. In addition, usage is included, and the user cannot get information about electricity usage. Therefore, considering these issues, we can offer smart products that will attract the attention of many businesses. This paper examines the analysis of IoT-based EV charging and monitoring. This project focuses on remote electrical parameters such as electricity, current and frequency and transmits the results of this time and the temperature of the EV charging station over the IOT network using the IOT modem/phone number. Users can send commands based on IoT messages to read remote electronic devices. And the system's bill can also periodically (scheduled) send real-time electricity in the form of IOT.

Barriers to real-time electricity pricing: Evidence from New Zealand

This paper studies the introduction of real-time electricity pricing in the New Zealand residential retail market to understand why its market share remained below 1.25%. We use rich panel data of all retail switches between 2014 and 2018 and an unexpected wholesale price spike to study adoption and attrition. Exploiting the staggered roll-out of real-time pricing in different locations we find that attrition decreases with experience. We also find that prospective adopters are present biased. The combination of these findings explains why adoption stalled and shows that wholesale price spikes pose a serious threat to widespread adoption of real-time pricing.

Discussion on Incentive Compatibility of Multi-Period Temporal Locational Marginal Pricing

In real-time electricity markets, locational marginal prices (LMPs) can be determined by solving multi-interval economic dispatch problems to manage inter-temporal constraints (i.e., ramp rates). Under the current practice, the LMPs for the immediate interval are binding, while the prices for the subsequent intervals are advisory signals. However, a generator may miss the opportunity for higher profits, and compensatory uplift payments are needed at the settlement. To address these issues, the “temporal locational marginal pricing (TLMP)” that augments LMP by incorporating multipliers associated with generators’ reported ramp rates was developed. It was demonstrated that it would result in zero uplift payments, showing great potential as a good pricing scheme. Numerical examples also showed that “the generators had incentives to reveal their ramp rates truthfully”. In this paper, the incentive compatibility of TLMP with respect to ramp-rate reporting is discussed. Our idea is to develop numerical examples to investigate whether reporting the true ramp rates is the best option for generators. The results indicate that TLMP is not incentive compatible, and there are market-clearing scenarios where not reporting true ramp rates may be beneficial.

Administration strategy of energy management in smart grid: system view and optimization path

Power generation and transmission infrastructure is vulnerable to the interaction of various Distributed Generations (DG), which leads to the imbalance of power system operation, frequent voltage drops or spikes, and even power outages. This phenomenon not only wastes energy, but also affects grid security. The main reason is a delayed feedback of circuit failure and load changes, and the optimization of energy management system and path is an effective way to solve the above problems. In this paper, a method of multi-objective optimization based on ANFIS algorithm is proposed which can help to improve the demand response, energy storage and management of smart power grid, reduce the volatility of DGs, reducing electricity costs and improving energy efficiency. Firstly, based on the ANFIS algorithm, the distributed power generation control mode, inverter control, real-time electricity price calculation method, energy transfer and storage scheme are improved, and the optimization path of the energy management system is defined. Secondly, the advantages of ANFIS algorithm in response speed and running stability are verified by comparing with other algorithms. Finally, a distributed energy microgrid is constructed for simulation verification. The results show that :(1) ANFIS optimization algorithm has good adaptability in smart grid, and has advantages in large amount of data processing and information transmission; (2) The verification model based on ANFIS has strong elasticity and efficient response speed. The research results will help solve various problems in the smart grid, including establishing a clear energy management system path, maintaining the stable operation of the power system, providing users with more reasonable power plans and the lowest cost of electricity.