Automated Load Curve Data Cleansing in Power Systems

Peak-shaving is a very efficient and practical strategy for a day-ahead hydropower scheduling in power systems, usually aiming to appropriately schedule hourly (or in less time interval) power generations of individual plants so as to smooth the load curve while enforcing the energy production target of each plant. Nowadays, the power marketization and booming development of renewable energy resources are complicating the constraints and diversifying the objectives, bringing challenges for the peak-shaving method to be more flexible and efficient. Without a pre-set or fixed peak-shaving order of plants, this paper formulates a new peak-shaving model based on the mixed integer linear programming (MILP) to solve the scheduling problem in an optimization way. Compared with the traditional peak-shaving methods that need to determine the order of plants to peak-shave the load curve one by one, the present model has better flexibility as it can handle the plant-based operating zones and prioritize the constraints and objectives more easily. With application to six cascaded hydropower reservoirs on the Lancang River in China, the model is tested efficient and practical in engineering perspective.

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Load Curve Data Cleansing and Imputation Via Sparsity and Low Rank

IEEE Transactions on Smart Grid ◽

10.1109/tsg.2013.2259853 ◽

2013 ◽

Vol 4 (4) ◽

pp. 2347-2355 ◽

Cited By ~ 18

Author(s):

Gonzalo Mateos ◽

Georgios B. Giannakis

Keyword(s):

Low Rank ◽

Load Curve ◽

Data Cleansing

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Robust Nonparametric Regression via Sparsity Control With Application to Load Curve Data Cleansing

IEEE Transactions on Signal Processing ◽

10.1109/tsp.2011.2181837 ◽

2012 ◽

Vol 60 (4) ◽

pp. 1571-1584 ◽

Cited By ~ 26

Author(s):

Gonzalo Mateos ◽

Georgios B. Giannakis

Keyword(s):

Nonparametric Regression ◽

Load Curve ◽

Data Cleansing

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Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches

Applied Sciences ◽

10.3390/app9091723 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1723 ◽

Cited By ~ 12

Author(s):

Juncheng Zhu ◽

Zhile Yang ◽

Yuanjun Guo ◽

Jiankang Zhang ◽

Huikun Yang

Keyword(s):

Deep Learning ◽

Power Systems ◽

Unit Commitment ◽

Combustion Engine ◽

Load Forecasting ◽

Learning Approaches ◽

Short Term ◽

Load Curve ◽

Short Term Load Forecasting ◽

Gated Recurrent Units

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.

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Performance of the Grid-Connected PV System in LV Microgrid Operation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.512-515.137 ◽

2012 ◽

Vol 512-515 ◽

pp. 137-142

Author(s):

Yan Li ◽

Li Wang ◽

Pan Pan Jing ◽

Bin Bin Zhong ◽

Bu Han Zhang ◽

...

Keyword(s):

Power Systems ◽

Power System ◽

System Analysis ◽

Low Voltage ◽

Environmental Benefits ◽

Pv System ◽

Load Curve ◽

Power System Analysis ◽

Pv Penetration ◽

Flexible Operation

Microgrids are a future power system configuration providing clear economic and environmental benefits compared to the legacy power systems, as the Grid-Connected PV penetration increases, its reaction in Low Voltage (LV) microgrid has to be taken into account during relative system studies. This paper presents a mathematical model for the Grid-Connected PV, it’s developed by User Define (UD) module on Power System Analysis Software Package (PSASP), PV behavior under several typical weather and typical 1-day load curve is studied in detail, Flexible Operation Strategy to achieve the reasonable voltage level are both considered, PSASP simulation environment is used to analyze the probable operation scenarios of LV microgrid, useful conclusions are summarized at last.

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Intelligent Regulation Method for a Controllable Load Used for Improving Wind Power Integration

Energies ◽

10.3390/en11113085 ◽

2018 ◽

Vol 11 (11) ◽

pp. 3085 ◽

Cited By ~ 1

Author(s):

Jia-Jue Li ◽

Bao-Zhu Shao ◽

Jun-Hui Li ◽

Wei-Chun Ge ◽

Jia-Hui Zhang ◽

...

Keyword(s):

Power Systems ◽

Wind Power ◽

Load Curve ◽

Loop Control ◽

Wind Power Integration ◽

External Loop ◽

New Energy ◽

Regulation Method ◽

Scheduling Plan ◽

Regulation Model

Improving the safety and stability of power systems by adjusting the controllable load to improve the wind power integration has become a hot research topic. However, the methodology of accurately controlling the load and fundamentally improving the wind power integration capacity has yet to be studied. Therefore, this paper proposes an intelligent regulation method for a controllable load. This method takes the new energy consumption assessment as feedback, and it combines the wind power acceptance assessment and scheduling plan to form the internal and external loop control structure, and it derives the controllable load intelligent regulation architecture. The load curve is decomposed by an interactive load observer, and the load curve is adjusted by the interactive load controller according to a given standard, thereby improving the new energy acceptance capability. Finally, based on the actual grid operation data of a provincial power grid in Northeastern China, the source grid load balancing process and the interactive load regulation model of the wind power system are simulated. The above method verifies the validity and rationality of the proposed method.

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Data Model for Residential and Commercial Buildings. Load Flexibility Assessment in Smart Cities

Sustainability ◽

10.3390/su13041736 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1736

Author(s):

Simona-Vasilica Oprea ◽

Adela Bâra ◽

Răzvan Cristian Marales ◽

Margareta-Stela Florescu

Keyword(s):

Power Systems ◽

Data Model ◽

Smart Cities ◽

Renewable Energy Sources ◽

Weather Forecast ◽

Stable Operation ◽

Peak Reduction ◽

Load Curve ◽

The Usa ◽

Data Collections

Demand response (DR) programs were usually designed to provide load peak reduction and flatten the load curve, but in the context of rapid adoption of emerging technologies, such as smart metering and sensors, load flexibility will address current trends and challenges (such as grid modernization, demand, and renewables growth) encountered by the evolving power systems. The uncertainty of the renewable energy sources (RES) and electric vehicle (EV) fleet operation has increased the importance of load flexibility that can be managed to provide more support for the stable operation of power systems, including balancing. In this paper, we propose a data model to handle load flexibility and take advantage of its benefits. We also develop a methodology to collect and organize data, combining the consumption profile with several auxiliary datasets such as climate characteristics of the location, independent system operator (ISO) to which the consumer is affiliated, geographical coordinates, assessed flexibility coefficients, tariff rates, weather forecast for day-ahead flexibility forecast, DR-enabling technology costs, and DR programs. These multiple features are stored into a flexibility relational database and NoSQL database for large consumption data collections. Then, we propose a data processing flow to obtain valuable insights from numerous .csv files and an algorithm to assess the load flexibility using large residential and commercial profile datasets from the USA, estimating plausible values of the flexibility provided by two categories of consumers.

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Smoothing the Metropolis Electric Power Consumption Daily Schedule with Mass Use of Electric Vehicles

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.953-954.1402 ◽

2014 ◽

Vol 953-954 ◽

pp. 1402-1405

Author(s):

Alexander Tavlintsev ◽

Maria Shorikova ◽

Sergey Yuferev

Keyword(s):

Power Consumption ◽

Power Systems ◽

Electric Power ◽

Electric Vehicles ◽

Distribution Networks ◽

Electric Power Consumption ◽

Load Curve ◽

Electric Power Supply ◽

Daily Schedule ◽

Charging Stations

In connection with the increasing fuel costs and decreasing incomes during the crisis electric vehicles are becoming more and more popular with drivers. With mass growth of using the electrical vehicles a possibility of transmission congestion can take place. While charging the vehicle by means of residential distribution there is a risk of facing electric power supply degradation and local accident conditions in grids. One of the basic current problems is that of the load curve irregularity, i.e. the existence of the peak hours and minimums in demand of the electric power. In its turn the load curve irregularity can cause unacceptable frequency oscillations in power systems. The development of charging station systems will lead to the increasing of the morning and evening demand of the electric power. It requires key investments in generators designing and improving the distribution networks, which in its turn will cause limitations in the number of charging stations and the electric vehicles expansion. Cost differentiation depending upon charging duration time can become an incentive to use charging stations during the periods of the minimum electric power consumption. A possibility of the electric vehicles usage as a means of smoothing the electric power consumption daily schedule is shown in the article. The evaluation of rationality of the electric vehicles integration as a power component in the network was made as well.

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Advanced energy management system based on PV and load forecasting for load smoothing and optimized peak shaving of islanded power systems

E3S Web of Conferences ◽

10.1051/e3sconf/201911303001 ◽

2019 ◽

Vol 113 ◽

pp. 03001

Author(s):

Petros Iliadis ◽

Stefanos Domalis ◽

Athanasios Nesiadis ◽

Konstantinos Atsonios ◽

Spyridon Chapaloglou ◽

...

Keyword(s):

Renewable Energy ◽

Power Systems ◽

Energy Management ◽

Renewable Energy Sources ◽

Clean Energy ◽

Energy System ◽

Load Curve ◽

Dynamic Simulations ◽

Peak Shaving ◽

Conventional Fuel

Photovoltaic (PV) systems constitute one of the most promising renewable energy sources, especially for warm and sunny regions like the southern-European islands. In such isolated systems, it is important to utilize clean energy in an optimal way in order to achieve high renewable penetration. In this operational strategy, a Battery Energy Storage System (BESS) is most often used to transfer an amount of the stored renewable energy to the peak hours. This study presents an integrated energy management methodology for a PV-BESS energy system targeting to make the load curve of the conventional fuel based units as smooth as possible. The presented methodology includes prediction modules for short-term load and PV production forecasting using artificial neural, and a novel, optimized peak shaving algorithm capable of performing each day’s maximum amount of peak shaving and smoothing level simultaneously. The algorithm is coupled with the overall system model in the Modelica environment, on the basis of which dynamic simulations are performed. The simulation results are compared with the previous version of the algorithm that had been developed in CERTH, and it is revealed that the system’s performance is drastically improved. The overall approach proves that in such islanding systems, a PV-BESS is a suitable option to flatten the load of the conventional fuel based units, achieve steadier operation and increase the share of renewable energy penetration to the grid.

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HEURISTIC APPROACH FOR HETEROGENEOUS REDUNDANCY OPTIMIZATION IN MULTI-STATE SERIES-PARALLEL SYSTEM

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539307002684 ◽

2007 ◽

Vol 14 (04) ◽

pp. 327-359 ◽

Cited By ~ 5

Author(s):

RASHIKA GUPTA ◽

MANJU AGARWAL

Keyword(s):

Genetic Algorithm ◽

Power Systems ◽

System Reliability ◽

Real Life ◽

Parallel System ◽

Load Curve ◽

State Behavior ◽

Solution Quality ◽

Solution Approach ◽

The Cost

The paper presents a heuristic for series-parallel system, exhibiting multi-state behavior, with the objective to minimize the cost in order to provide a desired level of reliability. System reliability is defined as the ability to satisfy consumers demand and is presented as a piecewise cumulative load curve. The components are binary and chosen from the list of products available in the market, and are being characterized by their feeding capacity, reliability and cost. The solution approach makes use of heterogeneous collection of components to provide redundancy in a subsystem. The algorithm has been applied to power systems from the literature for various levels of reliability requirement. The heuristic offers a straightforward analysis and efficiency over genetic algorithm (GA) existing in the literature. Keeping in view the computational efficiency and the observed solution quality the proposed heuristic is appealing. As such, the heuristic developed is attractive and can be easily and efficiently applied to numerous real life systems.

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