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New Heuristic Approaches for Demand Side Management and XGBoost Based Load Forecasting in Smart Grid
Abstract
There is an exponential increase in the demand of the energy due to increasing electrical devices. This results in an increasing demand versus supply gap. Due to scarcity of fossil fuels (e.g., oil, gas and coal), global environmental concerns, the rise in demand and addition of multiple efficient power generating systems; reformation of the current energy system is imminent. Smart Grid (SG) is introduced to handle above mentioned challenges. Moreover, for the efficient use of SG, exact prediction about the future coming load is of great importance to the utility. It helps the utility to produce as much energy as needed. The objective of this work is to handle the load need in an adequate manner through coordination among appliances in a Smart Home (SH); and real-time information exchange between user and utility. In this research, we proposed two new home energy management systems that are using load shifting technique for demand side management to improve the energy consumption pattern in a SH. This work assesses the behavior of advising plans for real-time pricing and critical peak pricing schemes. Two different models for the scheduling of home appliances are proposed in this research. Both the models focuses on hourly scheduling of appliances in a SH while aiming daily electricity cost reduction, Peak to Average Ratio (PAR) minimization and user comfort maximization. Both these models are implemented at the electricity management controller level, installed in a SH within a SG architecture. In the first model the proposed scheme performance is compared with the crow search algorithm and Jaya algorithm. In the second model proposed scheme performance is compared with the strawberry algorithm and the earthworm optimization algorithm. The proposed schemes performance is assessed for PAR, user comfort and cost. Furthermore, we worked on forecasting load demand at the utility end, for exact required power generation. We used Extreme Gradient Boosting (XGBoost) for load prediction for the next 30 minutes using previous 7 days data recorded at the rate of 30 minutes time lag. For forecasting, in first step we use XGBoost for calculating feature importance, which is then used for feature selection. In next step we use XGBoost for forecasting the electricity load for single time lag, using the selected features. XGBoost perform extremely well for time series prediction with efficient computing time and memory resources usage. XGBoost based load prediction model performed very good for mean average percentage error metric.
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Energy management using demand side management (DSM) techniques plays a key role in smart grid (SG) domain. Smart meters and energy management controllers are the important components of the SG. A lot of research has been done on energy management system (EMS) for scheduling the appliances. The aim of current research is to organize the power of the residential units in an optimized way. Intelligent energy optimization techniques play a vital role in reduction of the electricity bill via scheduling home appliances. Through appliance’s scheduling, consumer gets feasible cost in response to the consumed electricity. The utility provides the facility for consumers to schedule their appliances for the reduction of electricity bill and peak demand reduction. The utility company is allowed to remotely shut down their appliances in emergency conditions through direct load control programs. A lot of research has been done on energy management system (EMS) for scheduling the appliances. In this work, an efficient EMS is proposed for controlling the load in residential units. Meta-heuristic algorithms have been used for the optimization of the user energy consumption schedules in an efficient way. Our proposed scheme is used to minimize the user waiting time. User waiting time is inversely proportional to the total cost and peak to average ratio (PAR). Simulation result shows the minimum user waiting time, however, the total cost is compromised due to the high demand of the load. In the end, our proposed scheme will be validated through simulations.
This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.
This research focuses on a decomposed-weighted-sum particle swarm optimization (DWS-PSO) approach that is proposed for optimal operations of price-driven demand response (PDDR) and PDDR-synergized with the renewable and energy storage dispatch (PDDR-RED) based home energy management systems (HEMSs). The algorithm for PDDR-RED-based HEMS is developed by combining a DWS-PSO-based PDDR scheme for load shifting with the dispatch strategy for the photovoltaic (PV), storage battery (SB), and power grid systems. Shiftable home appliances (SHAs) are modeled for mixed scheduling (MS). The MS includes advanced as well as delayed scheduling (AS/DS) of SHAs to maximize the reduction in the net cost of energy (CE). A set of weighting vectors is deployed while implementing algorithms and a multi-objective-optimization (MOO) problem is decomposed into single-objective sub-problems that are optimized simultaneously in a single run. Furthermore, an innovative method to carry out the diversified performance analysis (DPA) of the proposed algorithms is also proposed. The method comprises the construction of a diversified set of test problems (TPs), defining of performance metrics, and computation of the metrics. The TPs are constructed for a set of standardized dynamic pricing signal and for scheduling models for MS and DS. The simulation results show the gradient of the tradeoff line for the reduction in CE and related discomfort for DPA. Keywords: price driven demand response; dispatch of renewables and energy storage systems; decomposed-weighted-sum method; multi-objective particle swarm optimization; diversified performance analysis; advanced and delayed scheduling; testing of HEMS algorithms
A user's requirement and designing goal for a computing machine are to process and respond the requests in real time with cost efficiency. System modeling with efficient resource allocation develops time and cost efficient platform. The cloud has enhanced resources with resource sharing techniques for efficient processing. However, it suffers from long Response Time (RT), which can degrade the real time applications for smart grid. This paper introduces a fog computing layer between cloud and client to process their energy requests in the fog instead of the cloud in order to optimize the computational cost, processing time and RT. A hybrid service broker policy and modified honey bee colony optimization algorithm are proposed for efficient fog selection and balancing load requests on virtual machines in the fog. The six geographical regions are considered. In each region, two clusters of residential buildings have access to two fogs for processing of their requests. The Micro Grids (MGs) are introduced in the proposed system model between the fogs and the clusters for uninterrupted and cost-efficient power supply. The recurring cost of MGs and computing cost of the fogs make the system operation cost, which is added in the consumers' electricity bill. The simulation validate the efficient system operation cost, processing and response time for power consumers.
Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR).
Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.
The emergence of the Demand Response (DR) program optimizes the energy consumption pattern of customers and improves the efficacy of energy supply. The pricing infra-structure of the DR program is dynamic (time-based). It has rather complex features including marginal costs, demand and seasonal parameters. There is variation in DR price rate. Sometime prices go high (peak load) if the demand of electricity is more than the generation capacity. The main objective of DR is to encourage the consumer to shift the peak load and gets incentives in terms of cost reduction. However, prices remain the same for all the users even if they shift the peak load or not. In this work, Game Theory (GT)-based Time-of-Use (ToU) pricing model is presented to define the rates for on-peak and shoulder-peak hours. The price is defined for each user according to the utilize load. At first, the proposed model is examined using the ToU pricing scheme. Afterward, it is evaluated using existing day-ahead real-time pricing scheme. Moreover, shifting load from on-peak hours to off-peak hours may cause rebound peak in off-peak hours. To avert this issue, we analysis the impact of Salp Swam Algorithm (SSA) and Rainfall Algorithm (RFA) on user electricity bill and PAR after scheduling. The experimental results show the effectiveness of the proposed GT-based ToU pricing scheme. Furthermore, the RFA outperformed SSA.
Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting.
The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud–fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.
The emergence of the smart grid has empowered the consumers to manage the home energy in an efficient and effective manner. In this regard, home energy management (HEM) is a challenging task that requires efficient scheduling of smart appliances to optimize energy consumption. In this paper, we proposed a meta-heuristic based HEM system (HEMS) by incorporating the enhanced differential evolution (EDE) and harmony search algorithm (HSA). Moreover, to optimize the energy consumption, a hybridization based on HSA and EDE operators is performed. Further, multiple knapsacks are used to ensure that the load demand for electricity consumers does not exceed a threshold during peak hours. To achieve multiple objectives at the same time, hybridization proved to be effective in terms of electricity cost and peak to average ratio (PAR) reduction. The performance of the proposed technique; harmony EDE (HEDE) is evaluated via extensive simulations in MATLAB. The simulations are performed for a residential complex of multiple homes with a variety of smart appliances. The simulation results show that EDE performs better in terms of cost reduction as compared to HSA. Whereas, in terms of PAR, HSA is proved to be more efficient as compared to EDE. However, the proposed scheme outperforms the existing meta-heuristic techniques (HSA and EDE) in terms of cost and PAR. 1
Presently, the advancements in the electric system, smart meters, and implementation of renewable energy sources (RES) have yielded extensive changes to the current power grid. This technological innovation in the power grid enhances the generation of electricity to meet the demands of industrial, commercial and residential sectors. However, the industrial sectors are the focus of power grid and its demand-side management (DSM) activities. Neglecting other sectors in the DSM activities can deteriorate the total performance of the power grid. Hence, the notion of DSM and demand response by way of the residential sector makes the smart grid preferable to the current power grid. In this circumstance, this paper proposes a home energy management system (HEMS) that considered the residential sector in DSM activities and the integration of RES and energy storage system (ESS). The proposed HEMS reduces the electricity cost through scheduling of household appliances and ESS in response to the time-of-use (ToU) and critical peak price (CPP) of the electricity market. The proposed HEMS is implemented using the Earliglow based algorithm. For comparative analysis, the simulation results of the proposed method are compared with other methods: Jaya algorithm, enhanced differential evolution and strawberry algorithm. The simulation results of Earliglow based optimization method show that the integration of RES and ESS can provide electricity cost savings up to 62.80% and 20.89% for CPP and ToU. In addition, electricity cost reduction up to 43.25% and 13.83% under the CPP and ToU market prices, respectively.
Nowadays, automated appliances are exponentially increasing. Therefore, there is a need for a scheme to accomplish the electricity demand of automated appliances. Recently, many Demand Side Management (DSM) schemes have been explored to alleviate Electricity Cost (EC) and Peak to Average Ratio (PAR). In this paper, energy consumption problem in a residential area is considered. To solve this problem, a heuristic based DSM technique is proposed to minimize EC and PAR with affordable user’s Waiting Time (WT). In heuristic techniques: Bacterial Foraging Optimization Algorithm (BFOA) and Flower Pollination Algorithm (FPA) are implemented.
A demand response (DR) based home energy management systems (HEMS) synergies with renewable energy sources (RESs) and energy storage systems (ESSs). In this work, a three-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of HEMS. The proposed method provides the trade-off between the net cost of energy ( C E n e t ) and the time-based discomfort ( T B D ) due to shifting of home appliances (HAs). At step-1, primary trade-offs for C E n e t , T B D and minimal emissions T E M i s s are generated through a heuristic method. This method takes into account photovoltaic availability, the state of charge, the related rates for the storage system, mixed shifting of HAs, inclining block rates, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. A filtration mechanism (based on the trends exhibited by T E M i s s in consideration of C E n e t and T B D ) is devised to harness the trade-offs with minimal emissions. At step-2, a constraint filter based on the average value of T E M i s s is used to filter out the trade-offs with extremely high values of T E M i s s . At step-3, another constraint filter (made up of an average surface fit for T E M i s s ) is applied to screen out the trade-offs with marginally high values of T E M i s s . The surface fit is developed using polynomial models for regression based on the least sum of squared errors. The selected solutions are classified for critical trade-off analysis to enable the consumer choice for the best options. Furthermore, simulations validate our proposed method in terms of aforementioned objectives.
With the emergence of smart grid (SG), the consumers have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management (DSM). In this paper, we introduce generic home energy management control system (HEMCS) model having energy management control unit (EMCU) to efficiently schedule household load and integrate RESs. The EMCU is based on genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO) and our proposed genetic WDO (GWDO) algorithm to schedule appliances of single and multiple homes. For energy pricing, real time pricing (RTP) and inclined block rate (IBR) are combined, because in case of only RTP there is a possibility of building peaks during off peak hours that may damage the entire power system. Moreover, to control demand under the capacity of electricity grid station feasible region is defined and problem is formulated using multiple knapsack (MK). Energy efficient integration of RESs in SG is a challenge due to time varying and intermittent nature of RESs. In this paper, two techniques are proposed: (i) energy storage system (ESS) that smooth out variations in renewable energy generation and (ii) trading of the surplus generation among neighboring consumers. The simulation results show that proposed scheme can avoid voltage rise problem in areas with high penetration of renewable energy and also reduce the electricity cost and peak to average ratio (PAR) of aggregated load.
A smart grid (SG) is a modernized electric grid that enhances the reliability, efficiency, sustainability, and economics of electricity services. Moreover, it plays a vital role in modern energy infrastructure. The core challenge faced by SGs is how to efficiently utilize different kinds of front-end smart devices, such as smart meters and power assets, and in what manner to process the enormous volume of data received from these devices. Furthermore, cloud and fog computing provide on-demand resources for computation, which is a good solution to overcome SG hurdles. Fog-based cloud computing has numerous good characteristics, such as cost-saving, energy-saving, scalability, flexibility, and agility. Resource management is one of the big issues in SGs. In this paper, we propose a cloud–fog–based model for resource management in SGs. The key idea of the proposed work is to determine a hierarchical structure of cloud–fog computing to provide different types of computing services for SG resource management. Regarding the performance enhancement of cloud computing, different load balancing techniques are used. For load balancing between an SG user’s requests and service providers, five algorithms are implemented: round robin, throttled, artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization. Moreover, we propose a hybrid approach of ACO and ABC known as hybrid artificial bee ant colony optimization (HABACO). Simulation results show that our proposed technique HABACO outperformed the other techniques.
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.
The increasing demand for electricity and the emergence of smart grids have presented new opportunities for a home energy management system (HEMS) that can reduce energy usage. HEMS incorporates a demand response (DR) tool that shifts and curtails demand to improve home energy consumption. This system commonly creates optimal consumption schedules by considering several factors, such as energy costs, environmental concerns, load profiles, and consumer comfort. With the deployment of smart meters, performing load control using HEMS with DR-enabled appliances has become possible. This paper provides a comprehensive review on previous and current research related to HEMS by considering various DR programs, smart technologies, and load scheduling controllers. The application of artificial intelligence for load scheduling controllers, such as artificial neural network, fuzzy logic, and adaptive neural fuzzy inference system, is also reviewed. Heuristic optimization techniques, which are widely used for optimal scheduling of various electrical devices in a smart home, are also discussed.
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.
Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers’ energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly) and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively.
As the coupling between home energy management system (HEMS) and distribution system state estimation (DSSE) becomes stronger for smart distribution grid operations, unexpected HEMS data change can, through the distortion of the HEMS solution, deteriorate the DSSE performance. In this paper, we investigate the impact of data changes in the HEMS for multiple homes on three phase unbalanced DSSE. We develop a two-level sensitivity analysis framework based on the perturbed Karush-Kuhn-Tucker (KKT) conditions from the HEMS and DSSE optimization formulations. The developed framework is used to assess the impact of the HEMS data changes in a low voltage distribution network (at the first level) on the DSSE solutions in a medium voltage distribution network (at the second level). Using the sensitivity framework, system operators can quantify the sensitivity of DSSE to changes in various types of HEMS data (e.g., demand response signals, appliance parameters, and consumer comfort). Along with the HEMS data impact analysis, the proposed sensitivity approach is tested under different measurement redundancy for DSSE in an IEEE 13-bus MV distribution system with 12 smart households in a radial LV network.
The traditional power grid is inadequate to overcome modern day challenges. As the modern era demands the traditional power grid to be more reliable, resilient, and cost-effective, the concept of smart grid evolves and various methods have been developed to overcome these demands which make the smart grid superior over the traditional power grid. One of the essential components of the smart grid, home energy management system (HEMS) enhances the energy efficiency of electricity infrastructure in a residential area. In this aspect, we propose an efficient home energy management controller (EHEMC) based on genetic harmony search algorithm (GHSA)to reduce electricity expense, peak to average ratio (PAR), and maximize user comfort. We consider EHEMC for a single home and multiple homes with real-time electricity pricing (RTEP) and critical peak pricing (CPP) tariffs. In particular, for multiple homes, we classify modes of operation for the appliances according to their energy consumption with varying operation time slots. The constrained optimization problem is solved using heuristic algorithms: wind-driven optimization (WDO), harmony search algorithm (HSA), genetic algorithm (GA), and proposed algorithm GHSA. The proposed algorithm GHSA shows higher search efficiency and dynamic capability to attain optimal solutions as compared to existing algorithms. Simulation results also show that the proposed algorithm GHSA outperforms the existing algorithms in terms of reduction in electricity cost, PAR, and maximize user comfort
Home energy management system (HEMS) provides an effective solution to assist residential users in dealing with the complexity of dynamic electricity prices. This study proposes a new HEMS in contexts of real-time electricity tariff and high residential photovoltaic penetrations. First, the HEMS accepts user-specified residential energy resource operation restrictions as inputs. Then, based on the forecasted solar power outputs and electricity prices, an optimal scheduling model is proposed to support the decision making of the residential energy resource (RES) operations. For the scheduling of heating, ventilating, and air conditioning system, an advanced adaptive thermal comfort model is employed to estimate the user's indoor thermal comfort degree. For the controllable appliances, the ‘user disturbance value’ metric is proposed to estimate the psychological disturbances of an appliance schedule on the user's preference. The proposed scheduling model aims to minimise the future 1 day energy costs and disturbances to the user. A new biological self-aggregation intelligence inspired metaheuristic algorithm recently proposed by the authors (a natural aggregation algorithm) is applied to solve the model. Extensive simulations are conducted to validate the proposed method.
IIn this paper, we propose a home energy management system which employs load shifting strategy of demand side management to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to minimize electricity cost and peak to average ratio while maintaining user comfort through coordination among home appliances. In order to meet the load demand of electricity consumers, we schedule the load in day-ahead and real-time basis. We propose a fitness criterion for proposed hybrid technique which helps in balancing the load during On-peak and Off-peak hours. Moreover, for real-time rescheduling, we present the concept of coordination among home appliances. This helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of appliance. For this purpose, we formulate our realtime rescheduling problem as knapsack problem and solve it through dynamic programming. This study also evaluates the behavior of the proposed technique for three pricing schemes including: time of use, real-time pricing and critical peak pricing. Simulation results illustrate the significance of the proposed optimization technique with 95% confidence interval.
The smart grid plays a vital role in decreasing electricity cost through Demand Side
Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity
consumption cost via scheduling home appliances. However, user waiting time increases due to
the scheduling of home appliances. This scheduling problem is the motivation to find an optimal
solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user
waiting time. There are many studies on Home Energy Management (HEM) for cost minimization
and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple
parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time
was minimum for residential consumers with multiple homes. Hence, in this work, we propose an
efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently
developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA),
which can be used for electricity cost and peak load alleviation with minimum user waiting time.
The integration of a smart Electricity Storage System (ESS) is also taken into account for more
efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the
real-time electricity consumption pattern for every residence, i.e., every home has its own living
pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes
(apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in
terms of electricity cost estimation for both a single smart home and a smart building. In addition,
feasible regions are presented for single and multiple smart homes, which show the relationship
among the electricity cost, electricity consumption and user waiting time. Experimental results
demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms
of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost
and user waiting.
In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP) technique and two heuristic optimization techniques: genetic algorithm (GA) and binary particle swarm optimization (BPSO) for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features.
In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.
We propose and implement a dc microgrid with a fully decentralized control system, using the ICT concept of network overlays and peer-to-peer (P2P) networks. Decentralization not only concerns the physical systems and control logic but also the control structure which provides the network infrastructure on which Energy Management is carried out. In this study, we show how such decentralization can be achieved using P2P frameworks as underlying control structures and implemented a pure P2P to eliminate single points of failure. For this, a Direct Current Open Energy System (DC-OES) made of the interconnection of standalone dc nanogrids is used as underlying microgrid. The power flows between nanogrids are controlled by a decentralized exchange strategy: each household can request or respond to energy deals with its neighbours without requiring system-wide knowledge or control. Using dc combined with a layered, modular software allows loose coupling which increases flexibility and dependability. The system has been implemented and tested on a full-scale platform in Okinawa including 19 inhabited houses. Real data analysis as well as simulations demonstrate improvements in selfsufficiency compared to other types of systems. Resilience against utility blackouts is proven in practice.
The latency issue of cloud based system can degrade smart grid's real-time applications. This paper introduces a fog computing layer between the cloud and clients to provide energy management service in near real-time with optimised computing cost. In a region, in proposed system model, two clusters of residential buildings have access to two fogs for processing of their energy requests. A hybrid service broker policy and modified honey bee colony optimisation algorithm are proposed for efficient fog selection and balancing requests' load on virtual machines in the fog. The micro grids (MGs) are introduced in the system model between the fogs and the clusters for uninterrupted and cost-efficient power supply. The recurring cost of MGs and computing cost of the fogs make the system operation cost, which is payable by the consumers. The simulation validate the efficient system operation cost, processing and RT for power consumers.
In smart grid, the minimum cost for power consumption is attained by scheduling the appliances load. Shifting the appliances load from on-peak to off-peak time reduces the cost in user's bill without compromising the load demand. In this paper, four scheduling algorithms are proposed by hybridizing Elephant Herding Optimization (EHO) with genetic, firefly, bacterial foraging and binary particle swarm optimization algorithms. Extensive simulations are performed to schedule the home appliances using proposed algorithms with three pricing tariffs: day ahead real time pricing, inclined block rates and critical peak pricing. The cost efficiency of optimized power consumption is analyzed. Results show that more cost is reduced with proposed hybrid algorithms as compared to the unscheduled and state-of-the-art algorithms.
The integration of information and communication 1 technologies in traditional grid brings about a smart grid. 2 Energy management plays a vital role in maintaining the 3 sustainability and reliability of a smart grid which in turn helps 4 to prevent blackouts. Energy management at consumers side 5 is a complex task, it requires efficient scheduling of appliances 6 with minimum delay to reduce peak-to-average ratio (PAR) 7 and energy consumption cost. In this paper, the classification 8 of appliances is introduced based on their energy consumption 9 pattern. An energy management controller is developed for 10 demand side management. We have used fuzzy logic and 11 heuristic optimization techniques for cost, energy consump-12 tion and PAR reduction. Fuzzy logic is used to control the 13 throttleable and interruptible appliances. On the other hand, 14 the heuristic optimization algorithms, BAT inspired and flower 15 pollination, are employed for scheduling of shiftable appliances. 16 We have also proposed a hybrid optimization algorithm for 17 the scheduling of home appliances, named as hybrid BAT 18 pollination optimization algorithm. Simulation results show a 19 significant reduction in energy consumption, cost and PAR. 20
Microgrid is a key enabling solution to future smart grids by integrating distributed renewable generators and storage systems to efficiently serve the local demand. However, due to the intermittent and uncertainty of distributed renewable energy, the reliability and economic operations of microgrid are facing increasing new challenges. Traditionally, economic dispatch issue is considered as solving an off-line or on-line optimization problem whose objective function is prior known. However, accurate and determined function expression is difficult to formulate, and wrong expression may result in waste of electricity cost and causing security issues. Thus, it is desirable to reformulate the economic dispatch problem, and solve it in a data-driven way. This paper proposes a data-driven energy management solution based on Bayesian-optimization-algorithm (BOA) for a single grid-connected home microgrid. The proposed solution formulates the optimization problem without a closed-form objective function expression, and solves it using BOA based data-driven framework. The proposed solution is a kind of black box function sequential global optimization strategy, and does not require derivative operation on the objective function. Besides, it can also solve the microgrid operation and parameter prediction uncertainty. Simulation results demonstrate the effectiveness of the proposed solution.
Advances in metering and two-way communication technologies foster the studies of Home Energy Management System (HEMS). This study proposes a new HEMS, which optimally schedules the distributed residential energy resources (DRERs) in a smart home environment with varying electricity tariff and high solar penetrations. The uncertainties of solar power output are captured by using Monte Carlo sampling technique to generate multiple solar output scenarios based on the probabilistic solar radiation model. The homeowner's rigid and elastic restrictions on the operations of the automatically controlled household appliances are modelled. Based on this, an optimal DRER scheduling model is proposed to minimise the home operation cost while taking into account the homeowner's requirements. A new heuristic optimisation algorithm recently proposed by the authors, i.e. natural aggregation algorithm, is used to solve the proposed model. Simulations based on real Australian solar data are conducted to validate the proposed method.
The ever-increasing load demand of the residential sector gives rise to concerns such as-decreased quality of service and increased demand-supply gap in the electricity market. To tackle these concerns, the utilities are switching to smart grids (SGs) to manage the demand response (DR) of the connected loads. However, most of the existing DR management schemes have not explored the concept of data analytics for reducing peak load while taking consumer constraints into account. To address this issue, a novel data analytical demand response (DADR) management scheme for residential load is proposed in this paper with an aim to reduce the peak load demand. The proposed scheme is primarily based on the analysis of consumers' consumption data gathered from smart homes (SHs) for which factors such as-appliance adjustment factor, appliance priority index, etc. have been considered. Based on these factors, different algorithms with respect to consumer's and utility's perspective have been proposed to take DR. In addition to it, an incentive scheme has also been presented to increase the consumers' participation in the proposed scheme. The results obtained show that it efficiently reduces the peak load at the grid by a great extent. Moreover, it also increases the savings of the consumers by reducing their overall electricity bills.
This study investigates a novel control algorithm for the home energy management system (HEMS) to monitor and schedule the electrical appliances to apply in any conventional houses. This is to reduce the electricity consumption and consequently electricity cost in places with time-of-use (TOU) pricing model. A hardware implementation is executed in a testbed house premise using in-house built control system, smart plugs as well as PV system as the supplementary source. In this system architecture, the overall consumption of the electrical appliances that run on the grid is limited by the algorithm and kept under the certain value that is fixed depending on the total consumption of the building and the capacity of the battery. If the predefined limit is crossed, the new appliances are shifted to run on battery instead of shifting to other time, and thereby human comfort is less violated. In the worst case scenario, where the electrical appliances are assumed to consume the maximum amount of electricity, a prototype implementation of the proposed algorithm achieves up to 15% of electricity cost reduction and ensures the minimum sacrifice in dweller's comfort.
As the internet's footprint continues to expand, cybersecurity is becoming a major concern for both governments and the private sector. One such cybersecurity issue relates to data integrity attacks. This paper focuses on the power industry, where the forecasting processes rely heavily on the quality of the data. Data integrity attacks are expected to harm the performances of forecasting systems, which will have a major impact on both the financial bottom line of power companies and the resilience of power grids. This paper reveals the effect of data integrity attacks on the accuracy of four representative load forecasting models (multiple linear regression, support vector regression, artificial neural networks, and fuzzy interaction regression). We begin by simulating some data integrity attacks through the random injection of some multipliers that follow a normal or uniform distribution into the load series. Then, the four aforementioned load forecasting models are used to generate one-year-ahead ex post point forecasts in order to provide a comparison of their forecast errors. The results show that the support vector regression model is most robust, followed closely by the multiple linear regression model, while the fuzzy interaction regression model is the least robust of the four. Nevertheless, all four models fail to provide satisfying forecasts when the scale of the data integrity attacks becomes large. This presents a serious challenge to both load forecasters and the broader forecasting community: the generation of accurate forecasts under data integrity attacks. We construct our case study using the publicly-available data from Global Energy Forecasting Competition 2012. At the end, we also offer an overview of potential research topics for future studies.
With the development of smart grid, energy consumption on residence will play an important role in the electricity market, while the Home Energy Management System (HEMS) has huge potential to help energy conservation. In this study, a practical HEMS model with renewable energy, storage devices and plug-in electric vehicles, considering the battery sustainability and the full utilisation of the renewable energy, is first established. Then, according to the combinations of the genetic algorithm (GA) and the multi-constrained integer programming method, an improved GA is proposed, which goal is to minimise the electricity purchase and maximise the renewable energy utilisation. Finally, it is demonstrated by an example that the proposed method is significant in cost saving and reducing energy wastes. To verify the performances of the proposed algorithm, the numerical results indicate that the proposed algorithm has high computational efficiency and good robustness. In addition, it can avoid the disadvantages easy to trap at a local optimal point, and are insensitive to initial solutions. The effect of the storage device on system property and the sensitivity of cost savings versus demand response, size of the battery, and the electricity price sell to the grid are also analysed.
The increasing demand for power in the Electrical Power System (EPS) causes a significant increase of power in the daily load curve as well as transmission line overload. The large variability in energy consumption in the EPS combined with unpredictable weather events can lead to a situation in which, to save the stability of the EPS, power limits must be introduced or even industrial customers in a given area have to be disconnected, which causes financial losses. Nowadays, a Transmission System Operator (TSO) is looking for additional solutions to reduce peak power, because existing approaches (mainly building new intervention power units or tariff programs) are not satisfactory due to the high cost of services in combination with an insufficient power reduction effect. The paper presents an approach to reduce peak loads with the use of Home Area Network (HAN) systems installed at residential units. The algorithm of the HAN system, executed by the HAN controller, is modeled using Unified Modeling Language (UML). Then using model transformation techniques, the UML model is translated into Verilog description, and is finally implemented in the Field Programmable Gate Array (FPGA). The advantages of the proposed approach are that with only a small loss of residential user comfort, there is a gain in energy reduction for a relatively small cost, an effective and convenient design of the HAN algorithm, and the flexible maintenance of HAN systems. The latter gain is possible thanks to using modern FPGAs, which allow for dynamic reconfiguration of the HAN controller. It means that a HAN algorithm of a selected user can be exchanged without power interruption of other residential users. A practical example illustrating the proposed approach and a calculation of the potential gains from its implementation are also presented.
This work studies the problem of appliances scheduling in a residential unit. An appliance-scheduling model for the home energy management system (HEMS) is established based on day-ahead electricity prices and photovoltaic (PV) generation. The HEMS receives the meter data and calculates the scheduling strategies, then the HEMS sends control signals to achieve the on/off control of the appliances through the ZigBee (a wireless communication technology with low power consumption in short distance). The study starts with a view to minimizing the summation of the electricity payments, the consumer's dissatisfaction (DS), and the carbon dioxide emissions (CDE), and the constraints specify the restrictions on the operating time and the power consumption of the appliances. A cooperative multi-swarm particle swarm optimization (PSO) algorithm is adopted to solve the combinational optimization problem. The appliances can be categorized into shiftable and non-shiftable appliances. For the shiftable appliances, the start time and power of the appliances can be scheduled flexibly in the case of the announced electricity prices. Furthermore, the plug-in hybrid electric vehicle (PHEV) is introduced to charge or discharge for energy management. Specially, the ability of selling electricity (SE) to the power grid is studied for appliances scheduling. Finally, the simulation results demonstrate that the cooperative multi-swarm PSO algorithm shows good convergence performance under different scenarios. Moreover, The electricity payments can be reduced by considering the carbon dioxide emissions in the objective function and selling electricity to the power grid, which also achieves the peak load curtailment.
The evolution in microgrid technologies as well as the integration of electric vehicles (EVs), energy storage systems (ESSs) and renewable energy sources (RESs) will all play a significant role in balancing the planned generation of electricity and its real-time use. We propose a real-time decentralized demand-side management (RDCDSM) to adjust the real-time residential load to follow a pre-planned day-ahead energy generation by the microgrid, based on predicted customers' aggregate load. A deviation from the predicted demand at the time of consumption is assumed to result in additional cost or penalty inflicted on the deviated customers. To develop our system, we formulate a game with mixed strategy which in the first phase (i.e., prediction phase) allows each customer to process the day ahead raw predicted demand to reduce the anticipated electricity cost by generating a flattened curve for its forecasted future demand. Then, in the second stage (i.e., allocation phase), customers play another game with mixed strategy to mitigate the deviation between the instantaneous real-time consumption and the day-ahead predicted one. To achieve this, customers exploit renewable energy and energy storage systems and decide optimal strategies for their charging/discharging, taking into account their operational constraints. RDCDSM will help the microgrid operator better deals with uncertainties in the system through better planning its day-ahead electricity generation and purchase, thus increasing the quality of power delivery to the customer. We evaluate the performance of our method against a centralized allocation and an existing decentralized EV charge control non-cooperative game method both which relies on a day ahead demand prediction without any refinement. We run simulations with various microgrid configurations, by varying the load and generated power and compare the outcomes.
Home Energy Management (HEM) systems enable residential consumers to participate in Demand Response Programs (DRPs) more actively. However, HEM systems confront some practical difficulties due to the uncertain inherent of renewable energies as well as the uncertainty of consumers' behavior. Moreover, the consumers aim for the highest level of comfort and satisfaction in operating their electrical appliances; while technical limits of the appliances must be considered. Furthermore, DR providers aim at keeping the participation of consumers in DRPs and minimize the 'esponse fatigue' phenomenon in long-term. In this paper, a stochastic model of HEM system is proposed by considering uncertainties of EV availability and small-scale renewable energy generation. The model optimizes the customer's cost in different DRPs, while guarantees the habitants' satisfaction by introducing a response fatigue index. Different case studies indicate that implementation of the proposed stochastic HEM system can considerably decrease both the customers' cost and response fatigue.
Recently, Home Energy Management (HEM) controllers have been widely used for residential load management in a smart grid. Generally, residential load management aims {to reduce the electricity bills and also curtail the Peak-to-Average Ratio (PAR)}. In this paper, we design a HEM controller on the {basis} of four heuristic algorithms: Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO). Moreover, we proposed {a} hybrid algorithm which is Genetic BPSO (GBPSO). All the selected algorithms are tested with the consideration of essential home appliances in Real Time Pricing (RTP) environment. Simulation results show that each algorithm in the HEM controller reduces the electricity cost and curtails the PAR. GA based HEM controller performs relatively better in term of PAR reduction; it curtails approximately $34\%$ PAR. Similarly, BPSO based HEM controller performs relatively better in term of cost reduction {, as} it reduces approximately $36\%$ cost. Moreover, GBPSO based HEM controller performs better than the other algorithms based HEM controllers in terms of both cost reduction and PAR curtailment.
By locally solving an optimization problem and broadcasting an update message over the underlying communication infrastructure, demand response program based on the distributed optimization model encourage all users to participate in the program. However, some challenging issues present themselves, such as the existence of an ideal communication network, especially when utilizing wireless communication, and the effects of communication channel properties, like the bit error rate, on the overall performance of the demand response program. To address the issues, this paper first defines a Cloud-based Demand Response (CDR) model, which is implemented as a two-tier cloud computing platform. Then a communication model is proposed to evaluate the communication performance of both the CDR and DDR (Distributed Demand Response) models. The present study shows that when users are finely clustered, the channel bit error rate is high and the User Datagram Protocol (UDP) is leveraged to broadcast the update messages, making the optimal solution unachievable. Contradictory to UDP, the Transmission Control Protocol (TCP) will be caught up with a higher bandwidth and increase the delay in the convergence time. Finally, the current work presents a cost-effectiveness analysis which confirms that achieving higher demand response performance incurs a higher communication cost.
Current demand response (DR) programs focus on industrial consumers as they can provide a large magnitude of demand modification. In order to extend DR programs to the residential sector, aggregating service demands from a large number of residential consumers is necessary in order to achieve a sensible benefit to the power network. In this paper, we propose a methodology for residential demand aggregation, based on a multi-class queueing system. Each class represents demand blocks of a specific power level, time duration, and a service delay requirement. We use this model to minimize the cost of the appliances' aggregated power consumption under day-ahead pricing (DAP). Using realistic appliances’ data, we show that the proposed framework achieves a cost reduction that is close to the best achievable one.
With the rapid development of distributed generations (DGs) and interruptible loads (ILs), distribution network company can actively purchase electricity in market instead of playing as a traditional passive purchaser. This study proposes a stochastic bi-level model-based strategic trading model for an active distribution company (ADisCo) which operates the active distribution network (ADN) to maximise its profit in electricity market. Uncertainties pertaining to bidding and offering prices of other market rivals', the imbalance prices in the balance market and the productions of DGs are considered via stochastic programming. Besides, a linear ADN operation model is proposed to ensure ADN operation security within the stochastic programming model. The proposed model is initially formulated as a stochastic bi-level model, where the upper-level problem represents the maximisation of the profit of ADN operator, whereas the lower-level model represents the maximisation of the social welfare in clearing of market from the perspective of independent system operator. On the basis of the complementarity theory, the proposed model can be transformed into a mixed integer linear programming model. Case studies demonstrate the efficiency and effectiveness of the proposed strategic trading model for an ADisCo with DGs and ILs.
Short-term load forecasting (STLF) models are very important for electric industry in the trade of energy. These models have many applications in the day-today operations of electric utility(ies) like energy generation planning, load switching, energy purchasing, infrastructure maintenance and contract evaluation. A large variety of STLF models have been developed which trade-off between forecast accuracy and convergence rate. This paper presents an accurate and fast converging STLF (AFC-STLF) model for industrial applications in a smart grid. In order to improve the forecast accuracy, modifications are devised in two popular techniques: (1) mutual information (MI) based feature selection, and (2) enhanced differential evolution (EDE) algorithm based error minimization. On the other hand, the convergence rate of the overall forecast strategy is enhanced by devising modifications in the heuristic algorithm and in the training process of the artificial neural network (ANN). Simulation results show that accuracy of the newly proposed forecast model is 99.5% with moderate execution time, i.e., we have decreased the average execution of the existing Bi-level forecast strategy by 52.38%.
Demand response (DR) is one of the most promising solutions to efficient control of smart grids with renewable energy resources. Usually, DR programs are im- plemented by means of centralized control by power supply companies or independent system operators (ISOs). In con- trast, recently, the focus has been on decentralized control to enhance the efficient use of distributed energy resources especially on microgrids. This paper proposes a decen- tralized control system for DR. The key of the proposed method is a new decentralized algorithm for determining appropriate control signals (corresponding to prices and/or incentives) by using communication networks provided by smart meters. The effectiveness of the proposed method is illustrated by a numerical example.