Novel AI Based Energy Management System for Smart Grid With RES Integration

Kumar, Astitva; Alaraj, Muhannad; Rizwan, M.; Nangia, Uma

doi:10.1109/access.2021.3131502

Cited by 19 publications

(7 citation statements)

References 31 publications

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“…However, the reformulation strategy does not focus on energy expenditure and thus fails to examine the effect of different appliance systems. Even renewable sources are applied in microgrid operations using artificial intelligence (AI) technique to meet the expected demand in smart grid environmental process [3]. Since grid development techniques are mostly based on nonlinear programming techniques, a saving cost can be achieved at appropriate time series.…”

Section: Literature Surveymentioning

confidence: 99%

Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems

Teekaraman

Kirpichnikova

Manoharan

et al. 2022

Wireless Communications and Mobile Computing

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This article examines the effect of smart grid systems by implementing artificial intelligence (AI) technique with application of renewable energy sources (RES). The current state generation smart grid system follows a high demand on supply of equal energy load to all grid states. However, in conventional techniques, high demand is observed as manual operation is preformed and load problems are not solved within the stipulated time period due to lack of technological advancements. However, applications of AI in smart grid process reduces risk of operation as manual adjustments are converted to highly automated procedures. This type of automatic process identifies the fault location at stage 1 and diagnosis of identified faults will be processed at stage 2. The abovementioned two stage processes will be incorporated with two constant parameters as dummy load is produced to overcome high- to low-power flows. Additionally, a scrap model has been designed to reduce the wastage of power as 100 percent effective progress can be achieved for low- to high-power supplies. To detect the corresponding regression losses in the grid systems, support vector machine (SVM) which completely identifies the previous state loss in the system is integrated. Hence, to analyze the effectiveness of the SVM model, four different scenarios are evaluated and compared with heuristic algorithms, long short-term memory (LSTM), autoregressive indicated moving average (ARIMA), adaptive ARIMA, and linear regression models with distinct performance analysis that includes error in percentage values where a total efficiency of 81% is achieved for projected SVM in all power lines including large-scale systems as compared to existing approaches.

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Section: Literature Surveymentioning

confidence: 99%

Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems

Teekaraman

Kirpichnikova

Manoharan

et al. 2022

Wireless Communications and Mobile Computing

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“…A. DCH-MD (Through t dch ) Rule-1: The discharge amount of a BES is (P LD (t) − P dem−lm ) − P pv (t) as per equation (14).…”

Section: Proposed Rule-based Peak Shaving Managementmentioning

confidence: 99%

“…al. Its performance is compared with mixed integer programming (MILP) based EMS with improved results [14]. B. Papari et.…”

Section: Introductionmentioning

confidence: 99%

A Novel Dynamic Load Scheduling and Peak Shaving Control Scheme in Community Home Energy Management System Based Microgrids

et al. 2023

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Load scheduling and peak demand shaving are two critical aspects of the utility grid operation that help both the grid operators as well as end-users. This paper proposes a two-stage community home energy management system for microgrids. The first stage deals with the dynamic clustered community load scheduling scheme. Comparatively flatter power demand was attained using particle swarm optimization (PSO) incorporating user-defined constraints. The new arising or remaining peaks as a consequence of consumer constraints are catered to in the second stage. The second stage proposes a rule-based peak shaving management method for the photovoltaic (PV) systems that are connected to the grid and the battery energy storage systems. The proposed technique determines the dynamic demand and feed-in limits based on the estimations of the upcoming day's load demand and PV power profiles. Also, the study presents an optimal rule-based management technique for peak shaving of utility grid power that sets the charge/discharge day ahead schedules of the battery. For peak energy minimization, PSO is used to calculate the optimal inputs needed for implementing the appropriate rulebased management strategy. MATLAB is used to test the proposed method for different PV power and load demand patterns, thus, achieving an average improvement of 8.5%.

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“…AI supports a smoother integration of RES into the meshed power systems [8]. AI has the capacity to handle historical data, learn from the previous patterns, predict the output of various RES (solar, wind-based power plants) and assist grid operators in dispatching volatile generation, maintain system stability and mix RES with other sources [9]. AI also optimizes the operation of these systems by predicting energy generation, managing energy storage and minimizing downtime.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Bâra,

Oprea

2024

Int J Comput Intell Syst

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The energy transition to a cleaner environment has been a concern for many researchers and policy makers, as well as communities and non-governmental organizations. The effects of climate change are evident, temperatures everywhere in the world are getting higher and violent weather phenomena are more frequent, requiring clear and firm pro-environmental measures. Thus, we will discuss the energy transition and the support provided by artificial intelligence (AI) applications to achieve a cleaner and healthier environment. The focus will be on applications driving the energy transition, the significant role of AI, and collective efforts to improve societal interactions and living standards. The price of electricity is included in almost all goods and services and should be affordable for the sustainable development of economies. Therefore, it is important to model, anticipate and understand the trend of electricity markets. The electricity price includes an imbalance component which is the difference between notifications and real-time operation. Ideally it is zero, but in real operation such differences are normal due to load variation, lack of renewable energy sources (RES) accurate prediction, unplanted outages, etc. Therefore, additional energy has to be produced or some generating units are required to reduce generation to balance the power system. Usually, this activity is performed on the balancing market (BM) by the transmission system operator (TSO) that gathers offers from generators to gradually reduce or increase the output. Therefore, the prediction of the imbalance volume along with the prices for deficit and surplus is of paramount importance for producers’ decision makers to create offers on the BM. The main goal is to predict the imbalance volume and minimize the costs that such imbalance may cause. In this chapter, we propose a method to predict the imbalance volume based on the classification of the imbalance sign that is inserted into the dataset for predicting the imbalance volume. The imbalance sign is predicted using several classifiers and the output of the classification is added to the input dataset. The rest of the exogenous variables are shifted to the values from previous day d − 1. Therefore, the input variables are either predicted (like the imbalance sign) or are known from d − 1. Several metrics, such as mean average percentage error (MAPE), determination coefficient R2 and mean average error (MAE) are calculated to assess the proposed method of combining classification machine learning (ML) algorithms and recurrent neural networks (RNN) that memorize variations, namely long short-term memory (LSTM) model.

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Novel AI Based Energy Management System for Smart Grid With RES Integration

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 19 publications

References 31 publications

Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems

Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems

A Novel Dynamic Load Scheduling and Peak Shaving Control Scheme in Community Home Energy Management System Based Microgrids

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

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