Peak-to-average Ratio Reduction Research Articles

Optimizing Efficiency of Home Energy Management System in Smart Grid using Genetic Algorithm

A next-generation electrical power system known as the "Smart Grid" (SG) uses two-way communication to generate, use, and transport electrical energy. Demand Response (DR) is one of the SG's primary features (DR). Smart meters in DR transmit a pricing signal to the consumer, allowing them to adjust their demand in reaction to the corresponding price signals. Day-Ahead and Time-of-Use are the most widely used load scheduling schemes, however, they deviate from the Real time pricing scheme (RTP). Due to its erratic nature, integrating renewable energy sources like solar and wind is a difficult process in SG. Because of the fluctuations in both energy consumption patterns and power rates, the majority of current methods for managing demand are predicated either on day-ahead or time-of-use pricing rather than real-time pricing. This study describes a load scheduling system that uses a Genetic Algorithm (GA) to classify various users according to their energy use in a real-time pricing environment. Our load scheduling problem is formulated utilizing the knapsack mathematical formulation technique to reduce the electricity expenditure. In order to keep the grid stable and reduce costs, renewable energy is integrated with the grid's energy to lower the Peak-to-Average Ratio (PAR). The efficiency of the suggested algorithm in terms of electricity cost and PAR reduction is supported by simulation results.

Optimal Energy Management System of Isolated Multi-Microgrids with Local Energy Transactive Market with Indigenous PV-, Wind-, and Biomass-Based Resources

The availability of sustainable, efficient electricity access is critical for rural communities as it can facilitate economic development and improve the quality of life for residents. Isolated microgrids can provide a solution for rural electrification, as they can generate electricity from local renewable energy sources and can operate independently from the central grid. Residential load scheduling is also an important aspect of energy management in isolated microgrids. However, effective management of the microgrid’s energy resources and load scheduling is essential for ensuring the reliability and cost-effectiveness of the system. To cope with the stochastic nature of RERs, the idea of an optimal energy management system (EMS) with a local energy transactive market (LETM) in an isolated multi-microgrid system is proposed in this work. Nature-inspired algorithms such as JAYA (Sanskrit word meaning victory) and teaching–learning based optimization algorithm (TLBO) can get stuck in local optima, thus reducing the effectiveness of EMS. For this purpose, a modified hybrid version of the JAYA and TLBO algorithm, namely, the modified JAYA learning-based optimization (MJLBO), is proposed in this work. The prosumers can sell their surplus power or buy power to meet their load demand from LETM enabling a higher load serving as compared to a single isolated microgrid with multi-objectives, resulting in a reduced electricity bill, increased revenue, peak-average ratio, and user discomfort. The proposed system is evaluated against three other algorithms TLBO, JAYA, and JAYA learning-based optimization (JLBO). The result of this work shows that MJLBO outperforms other algorithms in achieving the best numerical value for all objectives. The simulation results validate that MJLBO achieves a peak-to-average ratio (PAR) reduction of 65.38% while there is a PAR reduction of 51.4%, 52.53%, and 51.2% for TLBO, JLBO, and JAYA as compared to the unscheduled load.

Temporal Preferences-Based Utility Control for Smart Homes

The residential sector contributes a large part of the energy to the global energy balance. To date, housing demand has mostly been uncontrollable and inelastic to grid conditions. Analyzing the performance of a home energy management system requires the creation of various profiles of real-world residential demand, as residential demand is complex and includes multiple factors such as occupancy, climate, user preferences, and appliance types. Average Peak Ratio (A2P) is one of the most important parameters when managing an efficient and cost-effective energy system. At the household level, the larger relative magnitudes of certain energy devices make managing this ratio critical, albeit difficult. Various Demand Response (DR) and Demand Side Management (DSM) systems have been proposed to reduce this ratio to 1. The main ways to achieve this are economic incentives, user comfort modeling and control, or preference-based. In this study, we propose a unique opportunistic social time approach called the Time Utility Based Control Feature (TUBCF), which uses the concept of a utility function from economics to model and control consumer devices. We propose a DR model for residential customers to reduce Peak-to-Average Ratio (PAR) and improve customer satisfaction by eliminating Appliance Wait Time (WTA) during peak periods. For PAR reduction and WTA, we propose a system architecture and mathematical formulation. Our proposed model automatically schedules devices based on their temporal preferences and considers six households with different device types and operational characteristics. Simulation results show that using this strategy can reduce A2P by 80% and improve user comfort during peak hours.

Optimality and PAR Reduction in Autonomous Demand Response: Evaluation and Billing Mechanisms

The main objective of this paper is to propose a more efficient, distributed, and multi-objective billing model that can be implemented in every smart meter in the grid for achieving optimality and fairness. First, we develop a new evaluation index to evaluate such as billing model not only in addressing fairness but also to minimize the cost and reduce the Peak-to-Average Ratio (PAR) in the load demand and subsequently the bill of each customer. Then, we study some of the billing models that exist in the literature and evaluate them with our evaluation index. Simulations are performed to test the performance of our model in terms of optimality, PAR reduction and fairness.

Optimal allocation of energy storage and solar photovoltaic systems with residential demand scheduling

Improvements to the current generation and distribution of electricity via demand side management (DSM) and storage systems are prevalent facing increasing energy demand and environmental implications of electricity generation. In this paper, a multi-level optimization model, which incorporates energy demand scheduler (DS), energy storage (ES) and solar photovoltaic (PV) panels amongst households, was developed so as to lower the peak-to-average ratio (PAR) of energy demand and reduce electricity bills. This model consists of three levels: (1) household consumption optimization (solo opt) using convex programming, (2) grid consumption optimization (base opt) via a game-theoretic framework, and (3) ES/PV allocation optimization using genetic algorithm (GA opt). This framework searches for the optimal allocation of ES/PV in a heterogeneous residential population subdivided into consumer groups by household sizes and income levels. A case study was performed with model parameters determined by referencing state-averaged electricity bills and electricity usage data from Texas, US. The results showed that GA opt can achieve bills savings of ~11% and a PAR reduction from 1.53 to 1.30 by allocating a non-trivial optimal combination of ES/PVs to the households. Another GA opt approach was adopted by minimizing PAR and found that PAR can be effectively reduced from 1.53 to 1.00 with bills savings of ~4%. Most significantly, it was observed that the optimal allocation differs from the free market equilibrium due to positive externalities and synergies when combining DSM together with ES/PVs.

MARLA-SG: Multi-Agent Reinforcement Learning Algorithm for Efficient Demand Response in Smart Grid

The population is sharply growing in the last decade, resulting in non-potential power requests in dense urban areas, especially with the traditional power grid where the system is not compatible with the infrequent changes. Smart grids have shown strong potential to effectively mitigate and smooth power consumption curves to avoid shortages by adjusting and forecasting the cost function in real-time in response to consumption fluctuations to achieve the desired objectives. The main challenge for the smart grid designers is to reduce the cost and Peak to Average Ratio (PAR) while maintaining the desired satisfaction level. This article presents the development and evaluation of a Multi-Agent Reinforcement Learning Algorithm for efficient demand response in Smart Grid (MARLA-SG). Also, it shows a simple and flexible way of choosing state elements to reduce the possible number of states, regardless of the device type, range of operation, and maximum allowable delay. It also produces a simple way to represent the reward function regardless of the used cost function. SARSA (State-Action-Reward-State-Action) and Q-learning schemes are used and attained PAR reduction of 9.6%, 12.16%, and an average cost reduction of 10.2%, 7.8%, respectively.

Fuzzy energy management controller and scheduler for smart homes

The integration of information and communication technologies in traditional grid brings about a smart grid. Energy management plays a vital role in maintaining the sustainability and reliability of a smart grid which in turn helps to prevent blackouts. Energy management at consumer's side is a complex task, it requires efficient scheduling of appliances with minimum delay to reduce peak-to-average ratio (PAR) and energy consumption cost. In this paper, the classification of appliances is introduced based on their energy consumption pattern. An energy management controller is developed for demand side management. We have used fuzzy logic and heuristic optimization techniques for cost, energy consumption and PAR reduction. Fuzzy logic is used to control the throttleable and interruptible appliances. On the other hand, the heuristic optimization algorithms, BAT inspired and flower pollination, are employed for scheduling of shiftable appliances. We have also proposed a hybrid optimization algorithm for the scheduling of home appliances, named as hybrid BAT pollination optimization algorithm. Simulation results show a significant reduction in energy consumption, cost and PAR.

Day Ahead Real Time Pricing and Critical Peak Pricing Based Power Scheduling for Smart Homes with Different Duty Cycles

In this paper, we propose a demand side management (DSM) scheme in the residential area for electricity cost and peak to average ratio (PAR) alleviation with maximum users’ satisfaction. For this purpose, we implement state-of-the-art algorithms: enhanced differential evolution (EDE) and teacher learning-based optimization (TLBO). Furthermore, we propose a hybrid technique (HT) having the best features of both aforementioned algorithms. We consider a system model for single smart home as well as for a community (multiple homes) and each home consists of multiple appliances with different priorities. The priority is assigned (to each appliance) by electricity consumers and then the proposed scheme finds an optimal solution according to the assigned priorities. Day-ahead real time pricing (DA-RTP) and critical peak pricing (CPP) are used for electricity cost calculation. To validate our proposed scheme, simulations are carried out and results show that our proposed scheme efficiently achieves the aforementioned objectives. However, when we perform a comparison with existing schemes, HT outperforms other state-of-the-art schemes (TLBO and EDE) in terms of electricity cost and PAR reduction while minimizing the average waiting time.

Exploiting Game Theoretic Based Coordination Among Appliances in Smart Homes for Efficient Energy Utilization

In this paper, a demand side management (DSM) scheme is used to make energy utilization more efficient. The DSM scheme encourages the consumer to change energy utilization patterns which benefit the utility. In return, the consumer gets some incentives from the utility. The objectives of the proposed DSM system include: electricity bill reduction, reduced peak to average ratio (PAR), and maximization of consumer comfort. In the proposed system, the electrical devices are scheduled by using elephant herding optimization (EHO) and adaptive cuckoo search (ACS) algorithms. Moreover, a new algorithm called hybrid elephant adaptive cuckoo (HEAC) is proposed which uses the features of both former algorithms. A comparison of these algorithms is also presented in terms of three performance parameters. The HEAC shows better performance as compared to EHO and ACS which is evident from the simulation results. Different electricity tariffs are introduced by the utility to provide incentives to the consumers. A regional based time of use (ToU) tariff is used to make the system effective for different types of regions. Moreover, this enables the consumers to act according to the regional environment. The coordination can play a very important role in cost reduction as well as in consumer comfort maximization. The coordination is incorporated among the electrical devices by using cooperative game theory (GT) and dynamic programming (DP). Extensive simulations are performed to show the effectiveness of the proposed scheme in terms of electricity utilization cost, PAR reduction, and consumer comfort maximization.

A Domestic Microgrid with Optimized Home Energy Management System

Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources (RESs). Microgrid efficiently and economically generates power for electricity consumers and operates in both islanded and grid-connected modes. In this study, we proposed optimization schemes for reducing electricity cost and minimizing peak to average ratio (PAR) with maximum user comfort (UC) in a smart home. We considered a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through multiple knapsack problem (MKP) then solved by existing heuristic techniques: grey wolf optimization (GWO), binary particle swarm optimization (BPSO), genetic algorithm (GA) and wind-driven optimization (WDO). Furthermore, we also proposed three hybrid schemes for electric cost and PAR reduction: (1) hybrid of GA and WDO named WDGA; (2) hybrid of WDO and GWO named WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system (BBS) was also integrated to make our proposed schemes more cost-efficient and reliable, and to ensure stable grid operation. Finally, simulations were performed to verify our proposed schemes. Results show that our proposed scheme efficiently minimizes the electricity cost and PAR. Moreover, our proposed techniques, WDGA, WDGWO and WBPSO, outperform the existing heuristic techniques.

Towards Efficient Energy Management and Power Trading in a Residential Area via Integrating a Grid-Connected Microgrid

Demand side management (DSM) is one of the most challenging areas in smart grids, which provides multiple opportunities for residents to minimize electricity cost. In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization. Furthermore, a comparative analysis is also performed to show the legitimacy and productiveness of CSA and SA.

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.

A new heuristically optimized Home Energy Management controller for smart grid

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.

COMMUNITY BASED HOME ENERGY MANAGEMENT SYSTEM

In a Smart Grid (SG) scenario, domestic consumers can gain cost reduction benefit by scheduling their Appliance Activation Time (AAT) towards the slots of low charge. Minimization in cost is essential in Home Energy Management Systems (HEMS) to induce consumers acceptance for power scheduling to accommodate for a Demand Response (DR) at peak hours. Despite the fact that many algorithms address the power scheduling for HEMS, community based optimization has not been the focus. This paper presents an algorithm that targets the minimization of energy costs of whole community while keeping a low Peak to Average Ratio (PAR) and smooth Power Usage Pattern (PUP). Objective of cost reduction is accomplished by finding most favorable AAT by Particle Swarm Optimization (PSO) in conjunction with Inclined Block Rate (IBR) approach and Circular Price Shift (CPS). Simulated numerical results demonstrate the effectiveness of CPS to assist the merger of PSO & IBR to enhance the reduction/stability of PAR and cost reduction.

Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes

This paper presents real time information based energy management algorithms to reduce electricity cost and peak to average ratio (PAR) while preserving user comfort in a smart home. We categorize household appliances into thermostatically controlled (tc), user aware (ua), elastic (el), inelastic (iel) and regular (r) appliances/loads. An optimization problem is formulated to reduce electricity cost by determining the optimal use of household appliances. The operational schedules of these appliances are optimized in response to the electricity price signals and customer preferences to maximize electricity cost saving and user comfort while minimizing curtailed energy. Mathematical optimization models of tc appliances, i.e., air-conditioner and refrigerator, are proposed which are solved by using intelligent programmable communication thermostat ( iPCT). We add extra intelligence to conventional programmable communication thermostat (CPCT) by using genetic algorithm (GA) to control tc appliances under comfort constraints. The optimization models for ua, el, and iel appliances are solved subject to electricity cost minimization and PAR reduction. Considering user comfort, el appliances are considered where users can adjust appliance waiting time to increase or decrease their comfort level. Furthermore, energy demand of r appliances is fulfilled via local supply where the major objective is to reduce the fuel cost of various generators by proper scheduling. Simulation results show that the proposed algorithms efficiently schedule the energy demand of all types of appliances by considering identified constraints (i.e., PAR, variable prices, temperature, capacity limit and waiting time).

Economical and Balanced Energy Usage in the Smart Home Infrastructure: A Tutorial and New Results

The smart home infrastructure features the automatic control of various household appliances in the advanced metering infrastructure, which enables the connection of individual smart home systems to a smart grid. In such an infrastructure, each smart meter receives electricity price from utilities and uses a smart controller to schedule the household appliances accordingly. This helps shift the heavy energy load from peak hours to nonpeak hours. Such an architecture significantly improves the reliability of the power grid through reducing the peak energy usage, while benefiting the customers through reducing electricity bills. This paper presents a tutorial on the development of the smart controller to schedule household appliances, which is also known as smart home scheduling. For each individual user, a dynamic programming-based algorithm that schedules household appliances with discrete power levels is introduced. Based on it, a game theoretic framework is designed for multi-user smart home scheduling to mitigate the accumulated energy usage during the peak hours. The simulation results demonstrate that it can reduce the electricity bill by 30.11% while still improving peak-to-average ratio (PAR) in the power grid. Furthermore, the deployment of smart home scheduling techniques in a big city is discussed. In such a context, the parallel computation is explored to tackle the large computational complexity, a machine assignment approximation algorithm is proposed to accelerate the smart home scheduling, and a new hieratical framework is proposed to reduce the communication overhead. The simulation results on large test cases demonstrate that the city level hierarchical smart home scheduling can achieve the bill reduction of 43.04% and the PAR reduction of 47.50% on average.

A generic demand-side management model for smart grid

The demand-side management (DSM) is one of the most important aspects in future smart grids: towards electricity generation cost by minimizing the expensive thermal peak power plants. The DSM greatly affects the individual users’ cost and per unit cost. The main objective of this research article is to develop a generic demand-side management (G-DSM) model for residential users to reduce peak-to-average ratio (PAR), total energy cost, and waiting time of appliances (WTA) along with fast execution of the proposed algorithm. We propose a system architecture and mathematical formulation for total energy cost minimization, PAR reduction, and WTA. The G-DSM model is based on genetic algorithm (GA) for appliances scheduling and considers 20 users having a combination of appliances with different operational characteristics. Simulation results show the effectiveness of G-DSM model for both single and multiple user scenarios. Copyright © 2015 John Wiley & Sons, Ltd.

Environment-Adaptive Concurrent Companding and Bias Control for Efficient Power-Amplifier Operation

The design of power-efficient orthogonal frequency division multiplexing (OFDM) transmitters suffers from a major bottleneck due to the high peak-to-average ratio (PAR) of OFDM signals as the efficiency of a linear RF power amplifier (PA) reduces drastically due to backoff requirements. In this paper, we propose a system-level approach for dynamic power reduction in OFDM transmitters with varying channels. The proposed methodology uses adaptive baseband companding/expanding of the OFDM signal along with concurrent PA rebiasing to save power. Using channel quality information for dynamic PAR adaptation, the proposed approach achieves a PAR reduction of as high as 7.25 dB, under favorable channels. This translates to a power savings of 5.5× compared with static PAs and 3.6× compared with adaptive PAs with only output-power adaptation.

PAR Reduction of Multicarrier Signals Using Injected Tone Constellation

Tone Injection (TI) can reduce the high peak-to-average ratio (PAR) which can substantially limit the performance of multicarrier systems without bandwidth loss. However, TI results in peak regrowth since it does not consider second peaks which can be higher than the peak after performing TI and also the average transmit power is increased because of huge constellation. In this paper, a no-rate loss PAR reduction technique, Injected Tone Constellation (ITC), is proposed along with an iterative algorithm to achieve the performance increase and to minimize the average transmit power without high complexity.

PSD-Constrained PAR Reduction for DMT/OFDM

Common to all DMT/OFDM systems is a large peak-to-average ratio (PAR), which can lead to low power efficiency and nonlinear distortion. Tone reservation uses unused or reserved tones to design a peak-canceling signal to lower the PAR of a transmit block. In DMT ADSL systems, the power allocated to these tones may be limited due to crosstalk issues with many users in one twisted pair bundle. This PSD limitation not only limits PAR reduction ability, but also makes the optimization problem more challenging to solve. Extending the recently proposed active set tone reservation method, we develop an efficient algorithm with performance close to the optimal solution.