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Load/Price Forecasting and Demand Side Management in Smart Grids
Abstract
The significant part of smart grid is to make smart grid cost-efficient by predicting
electricity price and load. To resolve the problem, three modules are incorporated
within the prediction model 1. Firstly, the fusion of Decision Tree (DT) and Random Forest (RF) are used for feature selection and to remove the redundancy
among feature. Secondly, Recursive Feature Elimination (RFE) is taken for feature extraction purpose that extracts the principle components and also used for
dimensionality reduction. Finally, to forecast load and price, Support Vector Machine (SVM) and Logistic Regression (LR) as a classifiers are used through which
we achieve good accuracy results in load and price prediction.
Similarly, to futher improve the prediction performance we proposed an Efficient Convolutional Neural Network (ECNN) and Efficient K-nearest Neighbour
(EKNN) in which the parameters are tuned. It may be difficult to deal with
huge amount of load data that is coming from the electricity market. To overcome this issue, we incorporated three modules in the proposed prediction model
2. The proposed model consists of feature engineering and classification. Feature
engineering is a two-step process (feature selection and feature extraction); for
the purpose of feature selection Mutual Information (MI) is used which reduces
the redundancy among features and for feature extraction RFE is used to extract
the principle features from the selected features and reduces the dimensionality
of features. Finally, after training the data-set and the removal of the duplicate
features load prediction is done by ECNN and EKNN. The ECNN and EKNN
outperforms better then traditional Convolutional Neural Network (CNN) and K-nearest Neighbour (KNN). The forecast performance is evaluated by comparing
the results with MAPE, RMSE, MAE and MSE. i.e. 10.8, 7.5, 7.15, and 10.4
respectively.
Many techniques are integrated in smart homes and buildings in residential sector
to make them efficient, and reliable. The demand of energy is raising day by day
due to the increase in population. To resolve this issue, we have implemented
Grey Wolf Optimization (GWO) using Time of Use (TOU) in proposed model 3;
this combination encourages the most efficient use of the system and can reduce
the overall costs for both the customers and utility. We also have compared the
results of GWO and Bacterial Foraging Algorithm (BFA). The appliances are
classified into three categories: Non-shiftable, Power-shiftable, and Time-shiftable
appliances on the basis of their operating period, hourly consumption and daily
requirement of each appliance. The scheduling mechanism is capable of achieving
the optimal operational time. Simulation results are presented to demonstrate the
effectiveness of optimization techniques.
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This paper presents a model for optimal energy management under the time-of-use (ToU) and critical peak price (CPP) market in a microgrid. The microgrid consists of intermittent dispatchable distributed generators, energy storage systems, and multi-home load demands. The optimal energy management problem is a challenging task due to the inherent stochastic behavior of the renewable energy resources. In the past, medium-sized distributed energy resource generation was injected into the main grid with no feasible control mechanism to prevent the waste of power generated by a distributed energy resource which has no control mechanism, especially when the grid power limit is altered. Thus, a Jaya-based optimization method is proposed to shift dispatchable distributed generators within the ToU and CPP scheduling horizon. The proposed model coordinates the power supply of the microgrid components, and trades with the main grid to reduce its fuel costs, production costs, and also maximize the monetary profit from sales revenue. The proposed method is implemented on two microgrid operations: the standalone and grid-connected modes. The simulation results are compared with other optimization methods: enhanced differential evolution (EDE) and strawberry algorithm (SBA). Finally, simulation results show that the Jaya-based optimization method minimizes the fuel cost by up to 38.13%, production cost by up to 93.89%, and yields a monetary benefit of up to 72.78% from sales revenue.
In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and we show how they lead to improvements in predictive accuracy. In addition, we also consider that, despite the large number of proposed methods for predicting electricity prices, an extensive benchmark is still missing. To tackle that, we compare and analyze the accuracy of 27 common approaches for electricity price forecasting. Based on the benchmark results, we show how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant. Finally, using the same results, we also show that: (i) machine learning methods yield, in general, a better accuracy than statistical models; (ii) moving average terms do not improve the predictive accuracy; (iii) hybrid models do not outperform their simpler counterparts.
Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.
Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.
In this paper, a Multi-agent based locally administrated Power Distribution Hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation and management of Battery Energy Storage Systems (BESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small scale BESSs (each owned by community users), which are connected together to form a unified-ESS. Proposed PDH offers a localized switching mechanism that takes decision, whether to buy electricity from utility or use unified-ESS. This decision is based on the price of electricity at “time of use” and “State of Charge” (SoC) of unified-ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective sub-meters. There is no buying or selling of power from PDH, there is power sharing with the concept of “no profit no loss”. The objective of proposed PDH is to limit the purchase of electricity on “high priced” hours from the utility. This not only benefits the utility at crucial hours, but also provide effective use of power at the demand side. The proposed Multi-agent System (MAS) depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering On-Peak, Off-Peak and mid-level (Mid-Peak) prices of a real-time price signal during 24 hours of a day. Results clearly show vital financial benefits of “sharing power economy” for end users and efficient use of power within the smart community.
In the operation of a smart grid (SG), day-ahead load forecasting (DLF) is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately predict the load of the next day with a fair enough execution time. Our proposed model consists of three modules; the data preparation module, feature selection and the forecast module. The first module makes the historical load curve compatible with the feature selection module. The second module removes redundant and irrelevant features from the input data. The third module, which consists of an artificial neural network (ANN), predicts future load on the basis of selected features. Moreover, the forecast module uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. Simulations are conducted in MATLAB to validate the performance of our newly-proposed DLF model in terms of accuracy and execution time. Results show that our proposed modified feature selection and modified ANN (m(FS + ANN))-based model for SGs is able to capture the non-linearity(ies) in the history load curve with 97.11% accuracy. Moreover, this accuracy is achieved at the cost of a fair enough execution time, i.e., we have decreased the average execution time of the existing FS + ANN-based model by 38.50%.
Demand response (DR) smart grid technology provides an opportunity for electricity consumers to actively participate in the management of power systems. Industry is one of the major consumers of electric power. In this study, we propose a DR energy management scheme for industrial facilities based on the state task network (STN) and mixed integer linear programming (MILP). The scheme divides the processing tasks in industrial facilities into nonschedulable tasks (NSTs) and schedulable tasks (STs), and takes advantage of distributed energy resources (DERs) to implement DR. Based on day-ahead hourly electricity prices, the scheme determines the scheduling of STs and DERs in order to shift the demand from peak periods (with high electricity prices) to off-peak periods (with low electricity prices), which not only improves the reliability of the electric power system, but also reduces energy costs for industrial facilities.
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.
Forecasting electricity prices plays a significant role in making optimal scheduling decisions in competitive electricity markets. Predominantly, price forecasting is performed from a “point forecasting” perspective, i.e., forecasting the exact values of future prices. However, in some applications, such as demand-side management, operation decisions are made based on certain price thresholds. It is, hence, desirable to obtain the “classes” of future prices, which can be cast as an electricity price classification problem. In this paper, we investigate the application and effectiveness of several data mining approaches for electricity market price classification. In addition, we propose a new data model for forming the initial data set for price classification. Simulation results for New York, Ontario, and Alberta electricity market prices are provided. Finally, the application of the generated numerical results to a demand-side management case study is demonstrated.
By 2050, about one third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international activities of daily living (ADL) or the French Autonomie Gerontologie Groupes Iso-Ressources (AGGIR) scales, by automatically classifying the different ADL performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, infrared presence sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors are then used to classify each temporal frame into one of the ADL that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing/undressing). This is done using support vector machines. We performed a 1-h experimentation with 13 young and healthy subjects to determine the models of the different activities, and then we tested the classification algorithm (cross validation) with real data.
Electricity price forecasting is a significant part of smart grid because it makes smart grid cost efficient. Nevertheless, existing methods for price forecasting may be difficult to handle with huge price data in the grid, since the redundancy from feature selection cannot be averted and an integrated infrastructure is also lacked for coordinating the procedures in electricity price forecasting. To solve such a problem, a novel electricity price forecasting model is developed. Specifically, three modules are integrated in the proposed model. First, by merging of Random Forest (RF) and Relief-F algorithm, we propose a hybrid feature selector based on Grey Correlation Analysis (GCA) to eliminate the feature redundancy. Second, an integration of Kernel function and Principle Component Analysis (KPCA) is used in feature extraction process to realize the dimensionality reduction. Finally, to forecast price classification, we put forward a differential evolution (DE) based Support Vector Machine (SVM) classifier. Our proposed electricity price forecasting model is realized via these three parts. Numerical results show that our proposal has superior performance than other methods.
The majority of the load forecasting literature has been on point forecasting, which provides the expected value for each step throughout the forecast horizon. In the smart grid era, the electricity demand is more active and less predictable than ever before. As a result, probabilistic load forecasting, which provides additional information on the variability and uncertainty of future load values, is becoming of great importance to power systems planning and operations. This paper proposes a practical methodology to generate probabilistic load forecasts by performing quantile regression averaging on a set of sister point forecasts. There are two major benefits of the proposed approach. It can leverage the development in the point load forecasting literature over the past several decades and it does not rely so much on high-quality expert forecasts, which are rarely achievable in load forecasting practice. To demonstrate the effectiveness of the proposed approach and make the results reproducible to the load forecasting community, we construct a case study using the publicly available data from the Global Energy Forecasting Competition 2014. Compared with several benchmark methods, the proposed approach leads to dominantly better performance as measured by the pinball loss function and the Winkler score.
http://cse.iitkgp.ac.in/~ayan.mondal/Paper/HoMeS.pdf
Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
Modeling and simulation of wind energy production in the smart-grid scenario
- Pérez-Vilarelle
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- Ayala
Pérez-Vilarelle, Laura, José L. Risco-Martín, and José L. Ayala. " Modeling and simulation of wind energy production in the smart-grid scenario". In
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An integer linear programming based optimization for home demand-side management in smart grid
- Ziming Zhu
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