Load Growth Research Articles

Analysis of Age Transformer Due to Annual Load Growth in 20 kV Distribution Network

Distribution transformer is a component in distributing electricity from distribution substations to consumers. Damage to distribution transformers causes continuity of customer service to be disrupted (power cut or blackout occurs). The length of the PLN electricity network requires a transformer to distribute electricity to serve consumers and how to maintain the transformer. The daily load curve of a peak load for housing, shops and factories / industries varies. Load served 200 kVA distribution transformer cannot serve the load on housing, shops and factories / industry. The method used is the replacement of a distribution transformer with a capacity of one stage greater or the replacement of a distribution transformer with a capacity of two levels larger. The distribution transformer carried out by the research is a capacity of 200 kVA replaced by 250 kVA. The ability of a distribution transformer cannot accommodate a load which will increase as an area is advanced. Observations made by calculating the age of the transformer by assuming the annual load growth (r) = 3% = 0.3. Annual peak load (P) = 1.8 p, u increase in oil temperature at peak load (θo = 96.21 0C; 84.16 0C). The increase in the hottest temperature above the oil cover, the increase in the temperature of the hottest place above the oil (θg = 20 0C; 20 0C). The ratio of the load loss to the nominal load excitation loss (Q = 3; 30). By assuming the values of these methods it can be estimated that the life of a distribution transformer is 20 kV, a capacity of 200 kVA is 18 years.

Laboratory Model Test to Explore the Bearing Mechanism of Composite Foundation in the Loess Area

Pile composite foundation can make good use of the bearing capacity of the soil and pile, which is widely used in the Chinese northwest loess area. However, the theory of pile composite foundation is far from sufficient, hindering its long-term development. Aiming at this problem, a laboratory model test of pile composite foundation in the loess area was conducted to explore the common working mechanism and variations of each bearing stage. Besides, the settlement of the single pile composite foundation was calculated by using the modified tangent modulus method, and the result was compared with the experimental data. The main results of this paper are as follows: Both in the single pile and single pile composite foundation, loading-settlement curves showed a trend of “elastic to elastoplastic to plastic,” accompanied by the appearance of plummeting point. Influenced by the pile group effect, the loading-settlement curve of the group pile composite foundation showed a slow-varying trend without an obvious breakdown point. Pile axis stress increased with the growth of upper load. At the beginning of loading, the pile axis stress indicated such a distribution that stress on both ends of the pile was larger than that in the middle of the pile. When reaching a certain load, the location of the biggest pile axis stress transferred to the pile top, and the pile axis stress decreased gradually as the pile became deep. The side friction resistance in the static load test of the single pile was always positive, whereas in the composite foundation of a single pile and a group of piles, negative side resistance appeared in the upper side of the neutral point. Pile-soil stress ratio in the depth of 12 cm changed with the upper load. The outcome calculated by the modified tangent modulus method had a relatively better consistency with experimental data if the upper load was not too large.

A fuzzy decision-making model for optimal design of solar, wind, diesel-based RO desalination integrating flow-battery and pumped-hydro storage: Case study in Baltim, Egypt

This paper aims to propose a conceptual design model for sustainable hybrid renewable stand-alone energy system (HRSES) to meet the electricity demand of a large-scale reverse osmosis desalination plant in Baltim, Egypt. The model investigates the feasibility of different HRSES alternatives and develop a fuzzy-based multicriteria decision-making model for meticulously selecting the optimal energy solution. Both zinc-bromine flow battery and turbine-pumped hydro energy storage technologies are integrated independently with wind, solar, and diesel power sources. Firstly, the proposed model uses HOMER software to execute an energy-economic-ecological optimization analysis for studying the practicability and components sizing of nine HRSES alternatives. Second, an integration between Fuzzy-AHP and Fuzzy-VIKOR decision-making methods is executed to choose the best design considering ten performance criteria. In the second stage, the fuzzy environment is engaged to expedite decision-makers to express their ratings in linguistic terms and to achieve more sensible and accurate results. Among ten feasible alternatives, the results reveal that the optimal system consists of 5 × 20-kW wind turbines, 328-kW photovoltaic array, 100-kW diesel generator, 112 batteries and 235-kW converter. This system has the best economic performance among all alternatives with least NPC, COE, and payback-period of $1,048,046, 0.101$/kWh and 1.1 yr, respectively. Besides, it has a treasured share of renewable of 95.55%; hence, it produces a realistic CO2 of 25,426.46 kg/year. Lastly, the sensitivity analysis illuminates that the future load growth and low-interest rate hurt upcoming investments while the projected reduction in the cost of energy storages has an encouraging influence on financing decisions.

Fatigue life prediction for orthotropic steel deck details with a nonlinear accumulative damage model under pavement temperature and traffic loading

Taking into account uncertainties in the nonlinear process of fatigue damage accumulation for the fatigue life prediction of the structural details in orthotropic steel decks, a novel fatigue damage regression analysis model and a new fatigue reliability assessment method based on the nonlinear fatigue model are developed and then used with reliability profiles to predict the fatigue life of details in the orthotropic steel deck. In the regression analysis model, both the long-term moving vehicle loads and the asphalt concrete (AC) pavement temperature work as the main factors. The monitored data of the long-term moving vehicle loads and AC pavement temperatures from the structural health monitoring systems installed on a Yangtze River cable-stayed bridge are first used to construct the prediction models of the vehicle loads and the AC pavement temperatures. An exponential model is then developed based on the strain measurements to quantify the relationship between daily averaged pavement temperatures, moving vehicle loads (the annual average hourly aggregated traffic volumes (AAHTV) or the annual average hourly aggregated traffic weight (AAHTW)) and the unit vehicle load induced fatigue, which is derived from a nonlinear fatigue damage accumulative model. Further, a limit equation based on the aforementioned exponential regression model and the nonlinear fatigue damage accumulative model for the fatigue life prediction, which takes into account both the traffic conditions and pavement temperature, is developed. Finally, this methodology is employed to predict the fatigue life of the structural details in orthotropic steel decks under different vehicle load growth patterns in a cable-stayed bridge.

Flexible expansion planning of distribution system integrating multiple renewable energy sources: An approximate dynamic programming approach

Distribution system expansion planning is a long-term process with sequential multistage uncertain factors, so flexible planning methods are required to deal with the various potential investment risks. This paper presents an approximate dynamic programming based flexible distribution system expansion planning model, in which the long-term system load growth uncertainty and short-term power fluctuation are both considered. A flexible investment strategy based on Markov decision process is developed, in which the planning decisions are made sequentially with consideration of both the current costs and future variable costs under long-term load growth uncertainty. This problem is formulated as a large-scale multistage stochastic programming model, which is intractable due to the “curse of dimensionality”. Thus, an approximate dynamic programming approach is used to decompose the original multistage optimization problem into sequential subproblems that can be easily solved. Case studies are carried out on a 24-node distribution system with three planning stages and a 54-node distribution system with five planning stages. Numerical results validate the feasibility and benefits of the proposed planning approach in obtaining a flexible expansion scheme, which can significantly reduce the investment risk and configure renewable energy equipment more reasonably.

Linking Mineral Nutrition and Fruit Quality to Growth Intensity and Crop Load in Apple

Diagnostic of the nutritional status in fruit trees can be estimated using different models. However, these models usually do not fully match the character of the perennial crops and their accuracy needs to be improved. This study aimed at deepening the understanding of the aspects controlling mineral content of macro- and micronutrients and fruit quality of the apple trees with respect to the crop load and vegetative shoot growth. The focus was given on description of the nutrient content variability in time and space, its relation to important physiological events, and conditions altering their gradient in leaf and fruit. The evaluation was carried out with six years old ’Golden Delicious’ apple trees on M9, planted in an experimental orchard. Flowering rate, total yield, shoot growth intensity, mean fruit weight and quality along with mineral content of leaves, and fruit in different development stages were analysed according to intensity of natural or regulated flowering and fruit set, respectively. We found that aside of macroelements, iron, and manganese depended on the crop load. For nitrogen, phosphorus, and potassium content, their gradient increased up to 10–25% within the sufficiency range as the trees tried to maintain certain level of these nutrients in leaves and fruit simultaneously during deficit. In contrary, the higher content of Mg, Fe, and Mn related to the avoidance of deficit through up to 20–30% higher uptake in trees with higher crop load. Phosphorus, potassium, and calcium content in short fruiting shoots was more stage dependent, showing relation with the timing of cessation of particular meristematic growth activity. If no intense deficit occurred, the trees kept unrestricted initial investment of nutrients in the fruit during cell division stage whatever the crop load was. Their content at the later development stages appeared to be more dependent upon the consumption by particular sink. These findings allowed to improve the interpretation of the fruit trees nutrition status due to adaptation of the reference nutrient content in low cropping trees by a reduction of 5–10% N and P, 10–15% Ca, as well as by an increase of 15–20% K content in leaves at the stage with fruit size of 20 mm, and without change in P content at the stage of about 70% of final fruit size.

Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data

In 2007, China surpassed the USA to become the largest carbon emitter in the world. China has promised a 60%–65% reduction in carbon emissions per unit GDP by 2030, compared to the baseline of 2005. Therefore, it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies. This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data. By applying the Exploratory Spatial-Temporal Data Analysis (ESTDA) framework, this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013. The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units. The results show that, firstly, high accuracy was achieved by the model in simulating carbon emissions. Secondly, the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82% and 5.72%, respectively. The overall carbon footprints and carbon deficits were larger in the North than that in the South. There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units. Thirdly, the relative lengths of the Local Indicators of Spatial Association (LISA) time paths were longer in the North than that in the South, and they increased from the coastal to the central and western regions. Lastly, the overall decoupling index was mainly a weak decoupling type, but the number of cities with this weak decoupling continued to decrease. The unsustainable development trend of China’s economic growth and carbon emission load will continue for some time.

Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA

This paper introduces a multi-stage dynamic transmission network expansion planning (MSDTNEP) model considering the N-1 reliability constraint. The integrated planning problem of N-1 security and transmission expansion planning is essential because a single line outage could be a triggering event to rolling blackouts. Two suggested scenarios were developed to obtain the optimal configuration of the Egyptian West Delta Network’s realistic transmission (WDN) to meet the demand of the potential load growth and ensure the system reliability up to the year 2040. The size of a blackout, based on the amount of expected energy not supplied, was calculated to evaluate both scenarios. The load forecasting (up to 2040) was obtained based on an adaptive neuro-fuzzy inference system because it gives excellent results compared to conventional methods. The linear population size reduction—Success-History-based Differential Evolution with semi-parameter adaptation (LSHADE-SPA) hybrid—covariance matrix adaptation evolution strategy (CMA-ES) algorithm (LSHADE-SPACMA)—is applied to solve the problem. The semi-adaptive nature of LSHADE-SPACMA and the hybridization between LSHADE and CMA-ES are able to solve complex optimization problems. The performance of LSHADE-SPACMA in solving the problem is compared to other well-established methods using three testing systems to validate its superiority. Then, the MSDTNEP of the Egyptian West Delta Network is presented, and the numerical results of the two scenarios are compared to obtain an economic plan and avoid a partial or total blackout.

Multi-year stochastic planning of off-grid microgrids subject to significant load growth uncertainty: overcoming single-year methodologies

The optimal design of off-grid microgrids in developing countries is difficult to achieve, as several political and socio-economic risks can hamper investments of private companies. Estimating the energy demand and its growth is a challenging task, subject to high uncertainty that rarely have been accounted for in multi-year simulations at hourly resolution. Besides, from a long-term perspective, the assets degradation can significantly affect the performance of stand-alone hybrid energy systems. In this paper, we address these challenges and propose a novel stochastic dynamic method to size microgrids, simulating with accuracy the system operation and considering the unavoidable uncertainty in load growth and the components ageing. A predefined scenario tree structure allows capturing the load growth uncertainty and obtaining different capacity expansion strategies for each scenario. An illustrative case study for an isolated power system in Kenya using data collected in 23 Kenyan microgrids is shown. The proposed stochastic formulation results in a considerable reduction of the size of components with respect to traditional single-year approaches. Savings in terms of Net Present Cost (NPC) are beyond 16-20% and the effects of assets degradation are about 6%. Results lead to recommend multi-year optimization tools, as single-year methodologies can hardly achieve the same performances.

A novel tree seed algorithm for optimal reactive power planning and reconfiguration based STATCOM devices and PV sources

Reactive power dispatch based on static synchronous compensator (STATCOM) and photovoltaic (PV) sources in coordination with reconfiguration strategy is vital task to ensure reliable operation of modern radial distribution (RD) systems. This paper introduces a novel tree seed algorithm (TSA) to improve the performance of modern RD power systems. A variant of TSA based on intelligent and flexible mechanism search is proposed to create an interactive equilibrium between exploration and exploitation during the search process to improve the global solution. Two objective functions, the total power loss and the margin reserve security are optimized under normal condition and critical load growth. To obtain reliable operation of the distribution system under critical situations, a reactive power dispatch based STATCOM devices is managed in coordination with the reconfiguration stage to find the best combination of sectionalizing switches and tie switches to be operated. The efficiency and particularity of the proposed distribution-planning strategy validated on two test systems, the radial 33 buses and the practical radial 250 buses (30 kV) of Sonelgaz Company located in Biskra in the south of Algeria. This radial electric network designed principally to deliver energy to several small and important agricultural consumers. Results obtained based on various test cases and scenarios using the proposed new planning strategy confirm the efficiency of the proposed planning strategy in terms of reduction of the total power loss, improvement voltage magnitude and enhancement of margin reserve.

Smart coordination of virtual energy storage systems for distribution network management

With an increasing penetration of solar photovoltaic (PV) resources in distribution networks, voltage regulation becomes an important issue. In addition, due to the growth in air conditioning load in summer days, overloading management draws researchers’ attentions as well. This paper proposes a two-level consensus-driven distributed control strategy to coordinate virtual energy storage systems (VESSs), i.e. residential households with air conditioners, to avoid the violation of voltage and loading which are regarded as part of the main power quality issues in future distribution network. In the lower level, the precise modelling of VESSs is firstly built, then VESSs are aggregated via aggregators for better participating in the control scheme. Once the violation occurs, a consensus-driven control scheme in the upper level will be initiated to eliminate the error. Required active power adjustment is shared among VESSs aggregators via sparse communication networks, without compromising end users’ thermal comfort. Changes in the dynamic communication network topology are investigated to demonstrate their impacts on system performance. Simulation results based on a practical system in New South Wales, Australia is used to demonstrate the proposed control scheme, which can effectively manage voltage and loading in a distribution network with scalability and robustness.

Calculation of Grid Investment Quota Based on Electrical Transmission and Distribution tariff

Electrical transmission and distribution tariff reform is an important part of the new round of electric power system reform. Under the supervision of transmission and distribution tariff, the optimization of investment scale will be the focus of power grid companies’ future work. The lack of investment capacity of power grid means that the grid can’t meet the load growth and the requirement for the quality of power supply, and the investment surplus is that the investment scale of grid is much ahead of the load growth and power quality demand of users, which will lead the electrical transmission and distribution tariff soar in the next supervision cycle, causing the investment contraction of power grid. Therefore, the power grid companies must determine the reasonable investment scale based on the transmission and distribution tariff.

Experimental study on load capacity of thermal generator unit in heating season

With the development of renewable energy and the rapid growth of thermal load, due to the influence of the mode of “determining power by heat”, the power grid dispatching in heating season can not accurately adjust the unit load, and the peak load regulation capacity of power grid is limited. Through the load capacity test of cogeneration units in heating season, the adjustment range and limiting factors of different types of units are obtained, which can be used as reference for power generation enterprises and power grid dispatching operation.

A novel distributed generation planning algorithm via graphically-based network reconfiguration and soft open points placement using Archimedes optimization algorithm

Hosting capacity (HC) is the mathematical interpretation for the maximum aggregated distributed generations (DGs) penetration among the distribution networks. DGs integration has been adopted as a cleaner energy resource, but it has major operational issues like overvoltage, harmonics, and others. In this work, a novel mathematical optimization algorithm is adopted for HC maximization via sequential network reconfiguration followed by soft open points (SOPs) placement. Besides, a novel SOPs allocation index is proposed to reduce the computational burden taken by the optimization techniques while controlling the discrete allocation variables. Thus, to ensure maximum penetration of DG units and optimum operation scheme, HC maximization is formulated as multi-objective optimization approach, while considering system performance indices as a portion of the objective function. The proposed optimization algorithm succeeded in maximizing the HC of the IEEE 33- node distribution network, the IEEE 69- node network, the 59-node Egyptian distribution network and the 135-node Brazilian distribution network efficiently while improving system performance indices. Further analysis is conducted on the studied distribution systems to assess the effect of load growth on system’s HC after allocating both SOPs and DGs.

Implementing single- and multi-year sensitivity analyses to propose several feasible solutions for meeting the electricity demand in large-scale tourism sectors applying renewable systems

The tourism sectors consume a high amount of electrical power, which in most cases is supplied by the electrical grid and diesel generator. As a result, the electricity supply of large-scale tourism sectors is an important challenge in many countries. In current study, the feasibility study for applying the hybrid renewable energy systems to provide the power demand in social areas is investigated. Since Iran has a lot of tourism areas with a high amount of electricity consumption, a popular and crowded tourism sector in the Qom (a city in the center of Iran) is selected as the case study. The electricity consumption of this social area is found to be 23,808.91 kWh/day, which in the current system is supplied by the electricity grid. Also, the values of cost of energy and carbon dioxide emissions of current system are calculated to be 0.0931$/kWh and 5,741,214 kg/year, respectively. Regarding the available renewable resources of Qom city and the space limitations of this tourism sector, several combined renewable scenarios are simulated and assessed applying the HOMER Pro simulator. In continue, to identify the optimal scenarios, the techno–economic–environmental evaluation is implemented. The hybrid photovoltaic (PV)/wind turbine (WT)/battery (Bat)/diesel generator (DG)/biogas generator (BG), and grid/PV/WT are found as the optimum configurations for the off-grid and grid-connected systems, respectively. It was found that the Grid/PV/WT has a net present cost of 6.63 M$ and a cost of energy of 0.0234 $/kWh. Also, the cost of energy and net present cost of hybrid PV/WT/Bat/DG/BG are accounted for 0.1251$/kWh and 18.1 M$, respectively. By using this stand-alone system instead of current system, the CO2 emissions reduce by 61.87% (2,189,231 kg/year). In following, to assess the effects of load growth and PV degradation on system performance, the multi-year module of this software is employed. Based on the obtained findings, by applying the hybrid PV/WT/Bat/DG/BG instead of the current system, the contribution of emissions penalties to the overall net present cost diminishes from 15.47% to 0.73%. Finally, the heat map sensitivity analyses are applied to the economic and environmental parameters.

Power Sharing in Island Microgrids

The growth of local renewable energy sources and heavy loads in power distribution networks, such as the increasing electric vehicles charging stations, causes several issues with a direct impact on the stability of the electrical grid. An attempt to overcome such issues is the microgrid concept, which has the grid structured into local sub-grids that manage their power and energy balancing. A microgrid may operate connected or disconnected from the main grid, being dynamically necessary to guarantee a power balancing between local loads and sources. Furthermore, as several power units are connected to the same microgrid, equity is also required in terms of power sharing. Current work explores a scenario of an island operation of a microgrid with multiple sources, including battery storage systems and sharing power with multiple loads, including electric vehicle chargers, a scenario appropriated to a city grid. A local control solution for a stable operation of the microgrid in terms of both power balancing and power sharing is presented and validated through numerical and experimental results.

Multi-objective two-stage adaptive robust planning method for an integrated energy system considering load uncertainty

An integrated energy system (IES) is considered as an effective approach to reduce carbon emissions, lower energy supply costs and increase system flexibility. As numerous energy conversion and storage devices with various features have been developed, determining their types and capacity size and optimizing the synergic action of all selected energy devices under load uncertainty have become challenging issues during the planning and designing of a new IES. To address these issues, a load uncertainty model is constructed by integrating the regional equivalent standard building-integrated load prediction method, the robust uncertainty set method and the fuzzy C-means clustering algorithm. Based on this uncertainty model, this paper proposes a planning method that incorporates the fuzzy multi-objective decision and two-stage adaptive robust optimization methods for handling these challenging issues. The multi-objective decision method enables comprehensive optimization based on the system's economic performance, carbon emissions and grid integration level. The two-stage adaptive robust optimization method aims to optimize the single-objective problem transformed by the multi-objective decision method. The planning method is implemented in an industrial park in the Baoding City of China. The influence of load budget uncertainty, annual load growth rate and energy purchase price is then investigated. The results show that the load budget uncertainty and annual load growth rate only affect the optimal capacity size of the selected devices, whereas the energy purchase price influences both the optimal device types and their capacity size. The optimal capacity size remains constant when the load budget uncertainty exceeds 3. This study provides a methodology to select the suitable device types and determine their capacity size for IES under load uncertainty, which will benefit the design of a new IES.

Impact of High-Power Heat Load and W Surface Carbidization on Its Structural-Phase Composition and Properties

This paper presents the results of a study on impact of high-power heat load and tungsten (W) surface carbidization on its structural-phase composition and physical-mechanical properties. In this regard, carbidization of a W surface was carried out by means of beam-plasma discharge in a simulation machine with plasma-beam installation. Certain data were obtained regarding temperature in control points of studied samples and temperature distribution throughout the monoblock element, made as a rectangle with an orifice for a cooling path, placed in a fusion reactor divertor. The paper demonstrates that changes in heat load power have an impact on microhardness, roughness, atomization of the carbidized W surface, and phase formation processes. It was established that a heat load q = 10 MW/m2 has very little effect on the elemental composition of the surface and structural-phase composition of W samples with a carbidized layer. Growth of thermal load up to q = 20 MW/m2 leads to a noticeable transformation of tungsten monocarbide (WC) into tungsten semicarbide (W2C) and cracking of the W sample surface.

Research on Evaluation of Input and Output of Physical Assets of Power Grid Enterprises Based on DEA Theory

With the continuous and rapid development of the national economy and the improvement of people's living standards, the construction of regional power grids is gradually increasing, and the corresponding investment will also increase. If the scale of investment is too small or lagging, it will inevitably fail to meet the requirements of economic and social development and load growth. It will also affect the long-term development of the enterprise and fail to achieve the expected investment benefits. However, if the scale of investment is too large or too advanced, it will inevitably cause waste of funds and excess resources, and will also cause business pressure. Therefore, it is necessary to carry out input-output efficiency evaluation research on regional power grids to determine whether the scale of power grid construction is appropriate and whether the input and output match. Therefore, this paper uses the DEA input-output theory, combined with the analysis of the actual asset investment objectives of power grid companies, to build a comprehensive input-output evaluation system, and optimally conduct empirical analysis based on the actual data of various units in a certain area, which can provide support and guide.

A New Medium and Long-Term Power Load Forecasting Method Considering Policy Factors

At present, China’s power load development is facing a new situation in which policies such as the new economic norm, industrial structure adjustment, energy conservation and emission reduction, etc. are being deeply promoted, load growth in some areas begin to ease, and volatility of load gradually become prominent, which increases the difficulty of medium and long-term load forecasting. In this context, in view of multi-correlation, uncertainty of influence of policy factors on power load, in order to improve the accuracy of load forecasting under the influence of policy factors, and solve the problem that policy factors are ambiguous, difficult to be quantified, and difficult to be integrated into load forecasting model, a medium and long-term load forecasting model considering policy factors is proposed. First, by analyzing the influence of various policies on power load, a hierarchical policy influencing factor index system that combines macro and micro levels is constructed to systematically reflect the influence of economy and policies on load under the new situation. Then, in view of the traditional grey relational analysis model’s insufficient consideration of the difference of historical data and future power development situation, by respectively weighting historical periods and factor indexes, a quantification analysis model of power load influencing factors based on two-way weighted grey relational analysis is proposed to quantify the influence of various policy factors on power load, achieve the combination of subjective weighting and objective weighting,and obtain quantification weights. Finally, the weighted fuzzy cluster analysis method combined with weights is used to predict load under the influence of policy factors. The proposed model can better solve the difficulty of medium and long-term load forecasting caused by the volatility of load under the influence of policy factors, and is suitable for medium and long-term load forecasting under the background of policy changes. The analysis of calculation examples shows that compared with traditional grey relational analysis model, the quantification results of proposed methods are more realistic, compared with traditional prediction methods such as time series extrapolation and elasticity coefficient, proposed method has better prediction accuracy and engineering application value.