Thesis

New Heuristic Approaches for Demand Side Management and XGBoost Based Load Forecasting in Smart Grid

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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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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.
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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.
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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.
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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.
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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%.
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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.