ThesisPDF Available

Simulation Study for Optimized Demand Side Management in Smart Grid

Authors:
Thesis

Simulation Study for Optimized Demand Side Management in Smart Grid

Abstract and Figures

Smart grid is envisioned to meet the 21st century energy requirements in a sophisticated manner with real time approach by integrating the latest digital communications and advanced control technologies to the existing power grid. It will dynamically connect all the stake holders of smart grid through enhanced energy efficiency awareness corridor. Smart Homes (SHs), Home Energy Management Systems (HEMS) and effect of home appli- ances scheduling in smart grid are now familiar research topics in electrical engineering. Peak load management and reduction of Peak to Average Ratio (PAR) and associated methods are under focus of researchers since decades. These topics have got new dimensions in smart grid environment. This dissertation aims at simulation study for effective Demand Side Management (DSM) in smart grid environment. This work is mainly focused on optimal load scheduling for energy cost minimization and peak load reduction. This work comprehensively reviews the smart grid applications, communication technologies, load management techniques, pricing schemes and related topics in order to provide an insight to the environment required for dynamic DSM. Various network attributes such as Internet Pro- tocol (IP) support, power usage, data rate etc. are considered to compare the communications technologies in smart grid context. Techniques suitable for Home Area Networks (HANs) such as ZigBee, Bluetooth, Wi-Fi, 6LoWPAN and Z-wave are discussed and compared in context of consumer concerns and network attributes. A similar approach in context of utilities’ concerns is adopted for wireless communications techniques for Neighborhood Area Networks (NANs), which include WiMAX and GSM based cellular standards. Issues and challenges regarding dynamic DSM in smart grid have been discussed briefly. DSM is supposed to have a vital role in future energy management systems and is one of the hot research areas. This study presents detailed review and analytical comparison of DSM tech- niques along with related technologies and implementation challenges in smart grid. It also covers consumers and utilities concerns in context of DSM to enhance the readers’ intuition about the topic. Two major types of DSM schemes, incentive based and dynamic pricing based, have been discussed and compared analytically. Dynamic pricing based HEMS are emphasized as important tools for peak load reduction and consumers’ energy cost minimization. Dynamic pricing based HEMS and their associated optimization techniques along with analytical comparison of the latest schemes have been described. Comparison of DSM techniques and study of latest HEMS scheme provided the base for new ideas of partial baseline load and reserved interrupting load to formulate two unique energy cost minimization problems. These models resulted the following two solutions in which scheduling has been carried out through many different algorithms to reduce peak load and consequently the PAR. This work includes novel appliance scheduling solution named; Comprehensive Home Energy Management Architecture (CHEMA), with multiple integrated scheduling options in smart grid environment. Multiple layers of enhanced architecture are modeled in Simulink with embed- ded MATLAB code. Single Knapsack is used for scheduling and four different cases for cost reduction are modeled. Fault identification and electricity theft control have also been added along with the carbon foot prints reduction for environmental concerns. Simulation results have shown the peak load reduction of 22.9% for unscheduled load with Persons Presence Controller (PPC), 23.15% for scheduled load with PPC and 25.56% for flexible load scheduling. Simi- larly total cost reduction of 23.11%, 24% and 25.7% has been observed, respectively. Smart grid interface layer and load forecasting layers are not implemented in current work and will be focused in future work. Another novel comparative approach has also been proposed in this research, which investi- gates the effect of multiple pricing schemes and optimization techniques for cost minimization and peak load reduction. The proposed model uses multiple pricing schemes including Time of Use (ToU), Real Time Pricing (RTP) day ahead case and Critical Peak Pricing (CPP). Pro- posed optimization problem has been solved with multiple optimization techniques including Knapsack, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Knapsack is used with two options of limited slots scheduling and whole day scheduling. Comparative results of the multiple pricing and optimization schemes have been discussed. Results show that the best combination achieved with GA and CPP with 39.9223% cost reduction. PSO showed the 43.73% cost reduction with all the pricing schemes. The proposed schemes have many applications for peak load reduction and energy cost mini- mization to benefit consumers and utilities. A user can schedule his load using one of the op- tions provided in CHEMA according to his preferences. Similarly, maintenance activities can be accommodated without disturbing the pre-defined schedule by using reserved interrupting slots. In large buildings, reserved slots can be used to schedule heavy loads without generating a peak.
Content may be subject to copyright.
A preview of the PDF is not available
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Pakistan is facing severe energy crisis in spite of the fact that nature has blessed her with huge energy potential. Short fall of electricity supply in the country is increasing and has been recorded up to 4522 MW in 2010. This deficit reached to 7000 MW in May, 2011. A comprehensive review of Pakistan's energy sector is presented in this paper. Energy potential, major issues of energy sector and energy import options are discussed. Issues like poor management, combined cycle capacity, low hydro power share, circular debt and energy security have been covered. Energy potential assessment includes hydro solar, wind, coal, nuclear, hydrogen cells, geo-thermal, ocean resources and bio mass. Future prediction calculations are based upon country's current and world's average per capita energy consumption. Current oil and gas reserves of the country contribute to only 5 percent and 48.8 percent of the energy mix and at the current rate will be exhausted by 13 and 16 years respectively. The overwhelming dependence of the energy sector on imported fossil fuels may create a situation of energy security threat. However dependence upon the energy import options cannot be avoided in order to lessen the severity of energy crisis in near future. Some of the energy import options are: Turkmanistan, Afghanistan, Pakistan and India (TAPI); Iran, Pakistan and India (IPI) gas pipelines; Liquefied Natural Gas (LNG) from Qatar etc. On the other hand exploitation of vast renewable potential like hydro, solar and wind requires serious attention. Exploitation of indigenous coal resources would also be a key for solving energy crisis in the long run. In summary, this paper presents energy potential assessment in context of major issues, future predictions and impact of energy import options. This in turn provides a big, clear and brighter picture of the country's energy sector.
Article
Full-text available
In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modeled in a simple way which considers appliance class, user preferences and weather conditions. The Wind Driven Optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, Min-max Regret based Knapsack Problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with fast convergence rate, as compared to the existing techniques
Article
Full-text available
One of the most challenging problems associated with operation of Smart Micro-Grids is the optimal energy management of residential buildings with respect to multiple and often conflicting objectives. In this paper, a multi-objective mixed integer nonlinear programming model is developed for optimal energy use in a smart home, considering a meaningful balance between energy saving and a comfortable lifestyle. Thorough incorporation of a mixed objective function, under different system constraints and user preferences, the proposed algorithm could not only reduce the domestic energy usage and utility bills, but also ensured an optimal task scheduling and a thermal comfort zone for the inhabitants. To verify the efficiency and robustness of the proposed algorithm, a number of simulations were performed under different scenarios using real data; and the obtained results were compared in terms of total energy consumption cost, users’ convenience rates and thermal comfort level.
Conference Paper
We consider the problem of minimizing the electricity bill for a cellular base station powered by the smart grid and locally harvested renewable energy. We consider hourly-varying electricity prices made known one day ahead to the base station. We assume that the base station is equipped with a finite-capacity battery. We ensure that the instantaneous energy demand of the base station is satisfied and the constraints imposed by the battery are observed at any point in time. We propose several online energy management strategies that require only causal knowledge of the renewable energy generation and the power consumption profiles. We benchmark our proposed strategies against the optimal energy management policy which assumes perfect knowledge of all system parameters, e.g., base station energy usage and renewable energy generation, both in the past and the future. Simulation results show that the performance of our proposed online strategy deviates from the optimal by 2% at most.
Article
This paper presents a design and evaluates the performance of a power consumption scheduler in smart grid homes or buildings, aiming at reducing the peak load in them as well as in the system-wide power transmission network. Following the task model consist of actuation time, operation length, deadline, and a consumption profile, the scheduler linearly copies the profile entry or maps a combinatory vector to the allocation table one by one according to the task type, which can be either preemptive or nonpreemptive. The proposed scheme expands the search space recursively to traverse all the feasible allocations for a task set. A pilot implementation of this scheduling method reduces the peak load by up to 23.1 % for the given task set. The execution time, basically approximated by O(M NNP(3 M/2) NP) where M, N NP, and N P are the number of time slots, nonpreemptive tasks, and preemptive tasks, respectively, is reduced almost to 2% taking advantage of an efficient constraint processing mechanism which prunes a search branch when the partial peak value already exceeds the current best. In addition, local peak reduction brings global peak reduction by up to 16% for the home-scale scheduling units without any global coordination, avoiding uncontrollable peak resonance.
Article
The paper proposes a concept of power-consumption optimization in smart homes based on demand-side management (DSM) and using smart able to control home electrical devices by a wireless connection. The advantages of the concept are lower power consumption without reducing the users living comfort and enabling the users to actively participate in the smart-grid load shedding.
Article
This paper examines the circular debt problem in the Pakistani energy sector. After presenting the profile of the energy sector in Pakistan, the paper explains why circular debt has emerged in the sector. Two principal reasons are discussed for the circular debt problem: First, consumer tariffs were insufficient to recover the rising costs of power generation and the government (due to fiscal constraints) was not compensating PEPCO for the resulting losses. Second, PEPCO has faced significant problems in recovering dues from consumers. In order to resolve the circular debt problem, sharp adjustments in power tariffs may be required combined with the need by the government to explicitly recognize the costs of power subsidies in the budget.