# An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

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## Abstract

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## 1. Introduction

- We have provided an overview of smart grid with its architecture, demand side management techniques, and demand response programs used in smart grid including dynamic pricing schemes.
- Most of the existing surveys formulate RASP as a single objective problem focusing on cost optimization only. In this paper, we have presented the residential appliance scheduling problem using multi-objective optimization problem with three major optimization objectives which include electricity cost, peak-to-average ratio, and user satisfaction level. The user satisfaction parameter is formulated using the timing illustrations.
- We have presented the survey of most relevant studies done in the area of appliance scheduling with special focus on residential sector. The purpose of this overview is to give a more holistic view on RASP techniques including classical, heuristic, and meta-heuristic techniques with their objectives, contribution, and research gap.
- In this paper, we have classified the home appliances into shiftable interruptible, shiftable non-interruptible, and non-shiftable appliances on the basis of user’s comfort to provide clarity of classification. Several methods in existing literature do not consider non-shiftable category for appliance scheduling. It may lead to cross the highest allowable threshold for power consumption which can result in power failures in homes. Moreover, we have reviewed the existing methods which use more number of appliances which enhance the performance of the appliance scheduling algorithms.

## 2. Smart Grid

- Increasing demand for electricity.
- Shortfalls of generating units.
- Increasing power losses.
- Peak load management.
- Integration of renewable energy sources.
- Difficulties in meter reading.
- Customer satisfaction.
- Aging assets.
- Security of supply

#### 2.1. Characteristics of Smart Grid

- Efficient transmission of electricity.
- Reduced cost for utilities and thereby reducing cost for end-users.
- Faster electricity restoration after power failures.
- Reduce peak load which will help in reducing electricity rates.
- Use of RESs.
- Use of customer-owned local power generation systems like plug-in electric vehicles (PEVs).

#### 2.2. Consumer Perspective

- The consumer will no longer have to wait for monthly electricity bills to know how much electricity he/she uses. Using the smart meter, a timely and clear picture can be obtained.
- The consumer can be able to see the price of electricity at a particular hour so that he/she can manage the use of electricity. It will ensure the less usage when the price is high.
- Consumers can be able to generate their own power by putting rooftop solar which will help them save money and manage the electricity well and will also be able to contribute the surplus power to main grid.

#### 2.3. Smart Grid over Existing Grid

#### 2.4. Architecture of Smart Grid

#### 2.4.1. Generation

#### 2.4.2. Transmission

#### 2.4.3. Distribution

#### 2.4.4. End Users

#### 2.4.5. Service Provider

#### 2.4.6. Market

- Communication mechanism for prices between markets and end-user domain.
- Interoperability among providers (supply) and consumers (demand).
- Regulation of trading, retailing, and wholesaling of energy.
- Extension of pricing signals to each end-user.

#### 2.4.7. Operations

#### 2.4.8. Demand Response (DR)

#### 2.5. Demand Side Management (DSM)

#### Necessity of DSM

- Increasing usage of appliances during peak hours and peak energy deficit.
- Scarcity of fossil fuels.
- Global warming concern.
- Penetration of highly stochastic RESs.

- (a)
- It is a direct load control technique which focuses on decreasing the demand during peak hours. This technique is important where there is a problem of investments to install new generation units.
- (b)
**Valley Filling:**[30]It focuses on increasing consumption during off-peak hours. The demand in off-peak hours is achieved by encouraging end-users to consume the electricity by paying lower prices during that time.- (c)
**Load shifting:**[31]It is a widely used and most effective DSM technique. It is achieved by shifting the load from peak to off-peak hours. Customers are encouraged by paying cheaper tariffs during off-peak hours. This technique is the best solution from a utility point of view.- (d)
**Load Reduction:**[29]This technique is also called as strategic energy conservation. As shown in Figure 3d, the area under the new characteristic is reduced than the previous one. Thus, the peak can be reduced. Load reduction is achieved by using more efficient appliances which is also important at the global level.- (e)
- This technique is also called as load building. It increases the power consumption of users with a certain limit. It is achieved by encouraging users to spend the electricity to maintain the power system capacities and for the smooth operation of the power system.
- (f)
- In this technique, there is redistribution of loads to various time slots. Here, customers with flexible loads are identified who are ready to control their consumption in exchange for various incentives.

#### 2.6. Demand Response

- To reduce total electricity consumption.
- To reduce the total required power generation.
- To promote the idea of clean and green energy.

#### 2.6.1. Incentive-Based DR

#### 2.6.2. Price-Based DR

**Time-Of-Use Pricing (ToUP):**A ToUP is adjusted on different time blocks during a day (for example, four-hour block). The rate of ToUP is different at different blocks. Typically, a day is divided into three blocks which are peak, mid-peak, and off-peak. The price during peak periods is kept high by the utility. The cost of electricity is high when consumed during peak hours. Therefore, consumers are encouraged to minimize their usage in peak hours and shift it to mid-peak or off-peak hours to balance the load profile.**Critical Peak Pricing (CPP):**A CPP is implemented in homes only when electricity usage is more than 20 kW and where the facility of a smart meter is available which records the consumption after every fifteen minutes. If load demand is very high (more than 20 kW) during a specific period, then the period is called as critical period. The CPP is declared only when the day is forecasted as a critical period. This scheme is similar to ToUP. In a critical period, the normal peak pricing of ToUP is replaced by CPP. Thus, consumers have to shift their consumption out of the critical period to balance the power demand.**Real-Time Pricing (RTP):**The price in RTP varies depending on hours/days. This impacts the consumption of users during peak periods. A RTP can be classified into two schemes, namely, day-ahead pricing (DAP) and hourly pricing (HP). In the DAP scheme, utility publishes price details to users one day beforehand. For HP, it is provided every hour before consumption. The advantages of RTP are discussed in [55]. A RTP can be classified into two parts: first part records the consumer response based on real-time prices [56], while the second part records the consumer response based on optimized real-time prices published by utility [57].**Inclining Block Rate (IBR):**IBR scheme is adopted by pacific gas and electric, Southern California Edison, San Diego gas companies since 1980s. This pricing scheme charges more for each incremental block of consumption. For instance, first block of 50 kWh consumption costs 2 units, the second block of 50 kWh would cost 2.5 units and so on. In other words, it offers multi-level pricing. If the electricity consumption during a block exceeds a certain threshold, then price will also increase to a higher value for subsequent blocks. The motive behind IBR scheme is to encourage consumers to self-generate the electricity by using local generating resources, to conserve the energy efficiently, to distribute the load to different time periods of the day to avoid high electricity rates. Thus, this pricing scheme provides help in reducing PAR to achieve demand response.

## 3. Home Energy Management System (HEMS)

#### 3.1. Appliance Classification

**Non-Shiftable (NS) appliances:**These appliances cannot be shifted to other slots. They should remain ON continuously without any interruption for the entire duration for which they are scheduled. For instance, refrigerator, tube-lights.**Shiftable Non-Interruptible (SNI) appliances:**These appliances can be adjusted to any other time slots. However, these appliances cannot be interrupted during their functioning. For instance, washing machine, electric heater.**Shiftable Interruptible (SI) appliances:**These appliances are flexible and can be adjusted to other time intervals. They can be interrupted during their functioning. For instance, vacuum cleaner, dishwasher, etc.

#### 3.2. Datasets for Appliances Classification and Their Energy Consumption

## 4. Residential Appliance Scheduling Problem (RASP)

- To minimize electricity cost (EC).
- To minimize peak-to-average ratio (PAR).
- To maximize user satisfaction (US) level.

#### 4.1. Appliance Scheduling Model

#### 4.1.1. Minimization of Electricity Cost (EC)

#### 4.1.2. Minimization of Peak to Average Ratio (PAR)

#### 4.1.3. Maximization of User Satisfaction (US)

## 5. Optimization Techniques for RASP

#### 5.1. Classical Techniques

#### 5.1.1. LP and MILP Techniques

#### 5.1.2. NLP and MINLP Techniques

#### 5.2. Heuristic Approaches

#### 5.3. Meta-Heuristics Algorithms

#### 5.4. Discussion and Directions for Future Research

- This paper provides an overview of RASP techniques in three categories: (i) Classical techniques; (ii) heuristic approaches; (iii) meta-heuristic algorithms. Three major optimization objectives are considered which are minimization of EC, minimization of PAR, and maximization of the US level. Dynamic pricing schemes are used to evaluate the performances of these scheduling techniques.
- Classical techniques such as LP, NLP are capable of finding an exact solution to an optimization problem. RASP requires more complex computations because it involves the scheduling of multiple appliances at the same time. Thus, the classical techniques fail to solve such computationally expensive and high dimensional problems in an acceptable time.
- Heuristic approaches for RASP are used to find an approximate solution to an optimization problem. They are well designed, efficient, and faster techniques that are capable of finding a near-optimal appliances schedule for RASP. The heuristic approach used in [92] proved that the difference between optimally achievable cost and heuristic-based achieved cost is negligible.
- Many meta-heuristic algorithms such as ACO [63], GA [71], PSO [99] are inspired by natural phenomena. These algorithms explore the alternative meaning of optimizing RASP objectives and scheduling the power profile of home appliances at any hour of the day. These algorithms can be effective for a given instance of RASP. However, in some cases, because of the nature of convergence, these algorithms can take a long time without a satisfactory solution.
- Based on the conducted review, the most commonly studied optimization objectives for RASP are electricity cost, peak-to-average ratio, and user satisfaction level. Very few studies in the literature have optimized all three objectives simultaneously. In the future, there is a scope of proposing a new objective with novel fitness function which combines all the three objectives with potentially better results.
- RASP is also a scheduling problem in which appliances are required to be scheduled one after the other. The consumer prioritizes the appliances and schedules them in a time-wise fashion. The priority concept in appliance scheduling can be correlated with the scheduling of CPU in the operating system. Some of the known CPU scheduling algorithms are first-come-first-served, shortest job first, round robin, pre-emptive-based priority, non-pre-emptive-based priority algorithms. Thus, in the future, RASP can be studied concerning priority or above-mentioned scheduling algorithms.
- Since the phenomenon in nature/ecosystem exhibits optimization properties, the nature-inspired algorithms have come forward in the optimization domain. No study in the literature describes the exact simulation of meta-heuristics to appliances. Thus, there is a need for a step-by-step simulation of nature-inspired algorithms with an appliance scheduling problem.
- Demand response programs for the smart grid have been thoroughly studied in the literature. However, a detailed investigation is recommended in the demand response for the wholesale electricity market where bidding and competition for electricity are involved.
- All the heuristic approaches for RASP have not been explored in the literature. In this paper, we have reviewed heuristic approaches for appliance scheduling in detail. In the future, there is further scope of proposing a new heuristic approach for RASP with mathematical modeling.
- In literature, residential appliance scheduling is studied to a large extent. A similar overview is required in the case of the Industrial Appliance Scheduling Problem (IASP). Since very high loads are involved in industries, specific industries (For example, mining industry, printing industry) can be studied concerning appliance scheduling. Thus, there is a possible future scope in the industrial domain.

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

2TP | Two-Tier Pricing |

ACO | Ant Colony Optimization |

ADSM | Active Demand Side Management |

AMI | Advanced Metering Infrastructure |

ANN | Artificial Neural Network |

BFO | Bacterial Foraging Optimization |

CEMU | Central Energy Management Unit |

CIP | Customer Incentive Pricing |

CPP | Critical Peak Pricing |

CNLP | Constrained Non Linear Programming |

CSA | Crow Search Algorithm |

DAP | Day Ahead Price |

DLC | Direct Load Control |

DR | Demand Response |

DSM | Demand Side Management |

EC | Electricity Cost |

ECS | Energy Consumption Scheduler |

EDE | Enhanced Differential Evaluation |

EV | Electric Vehicles |

EWA | Earth Worm Algorithm |

FA | Firefly Algorithm |

FPA | Flower Pollination Algorithm |

GA | Genetic Algorithm |

GWO | Grey Wolf Optimization |

HEMS | Home Energy Management System |

HEMU | Home Energy Management Unit |

HP | Hourly Pricing |

HSA | Harmony Search Algorithm |

IBDR | Incentive-Based Demand Response |

IBR | Inclined Block Rate |

ICT | Information and Communication Technology |

IDSS | Intelligent Decision Support System |

IED | Intelligent Electronic Vehicles |

JOA | Jaya Optimization Algorithm |

LOT | Length of Operational Time |

LP | Linear Programming |

MDP | Markov Decision Process |

MILP | Mixed-Integer Linear Programming |

MINLP | Mixed-Integer Non-linear Programming |

MOMILP | Multi Objective Mixed-Integer Linear Programming |

NLP | Non Linear Programming |

NS | Non Shiftable Appliances |

OT | Operational Time |

PAR | Peak-to-Average Ratio |

PBDR | Price-Based Demand Response |

PEVs | Plug-in Electric Vehicles |

PIO | Pigeon Inspired Optimization |

PSO | Particle Swarm Optimization |

RASP | Residential Appliance Scheduling Problem |

RESs | Renewable Energy Sources |

RTO | Regional Transmission Operator |

RTP | Real Time Pricing |

RUOA | Runner Updation Optimization Algorithm |

SBA | Strawberry Algorithm |

SG | Smart Grid |

SI | Shiftable Interruptible appliances |

SM | Smart Meter |

SNI | Shiftable Non-interruptible appliances |

SSO | Single Swarm Optimization |

ToUP | Time-of-Use Pricing |

UCM | User Comfort Maximization |

US | User Satisfaction |

WDO | Wind Driven Optimization |

WT | Waiting Time |

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Characteristics | Existing Grid | Smart Grid |
---|---|---|

Technology | Uses completely electromechanical technology meaning it has no communication between devices and no regulation. | Uses completely digital technology facilitating remote control, self-regulation, increased communication between devices |

Metering Infrastructure | Manual reading of meters | Uses smart meter which makes consumers aware of their power consumption |

Communication | One way communication between utility and consumers to exchange power flow and information | Bi-directional communication between utility and consumer to exchange power flow and information. |

Generation | The power is generated from a centralized location. | Power is distributed from multiple substations and plants to balance the load. |

Sensors | The existing grid is not equipped to handle many sensors in power lines | SG allows multiple sensors to locate the problem. |

Monitoring | Energy distribution must be manually monitored | Self-monitoring using digital technology allowing balance power loads troubleshoot outages |

Fault management | Comes across with failures and blackouts | Converts them into adaptive and islanding mode |

Restoration | Manual restoration is required for repairing failures. | No manual interference is required, uses self-healing mechanism |

Category | Appliances | Power Rating | Duration/LOT |
---|---|---|---|

Non Shiftable Appliances | Refrigerator Television Tube Light Fan Air Conditioner Laptop Oven | 0.3 0.6 0.1 0.1 1.5 0.1 1.7 | 24 3 8 4 6 2 2 |

Shiftable Non-Interruptible Appliances | Washing Machine Electric Iron Water Heater | 3 1 1.1 | 6 3 6 |

Shiftable-Interruptible Appliances | Vacuum Cleaner Dishwasher Clothes dryer | 1.2 2.5 3 | 2 4 5 |

Sr No. | Scheme | Technique | Pricing | Objective of Scheme |
---|---|---|---|---|

1 | [88] | Price update interval-based heuristic approach | Dynamic | To study the effects of network delay and transmission error to achieve desired power load for residential users |

2 | [89,90] | Greedy strategy | RTP | To schedule the appliances one after the other |

3 | [91] | Aggregator-based heuristic approach | CIP | To schedule the appliances with optimization of aggregator profit |

4 | [92] | Greedy strategy | RTP, ToUP, 2TP | To minimize EC in-home IDSS |

5 | [93] | Heuristic solutions for each sub-problems | Fixed | To determine optimal appliance schedule to balance demand and supply |

6 | [94] | Artificial neural network | Dynamic | To maximize the use of local generation and enhance the performance of local energy in the residential sector |

7 | [95] | Markov decision-based approach | Dynamic | To reduce the energy expenses of consumers |

8 | [96] | Backtracking-based scheduling algorithm | Dynamic | To reduce the peak load in smart homes. |

9 | [97] | Load control strategy for optimization | RTP | To balance user comfort and consumer price preferences along with saving electricity bills. |

Sr No. | Scheme | Meta-Heuristic Algorithms | Pricing | Number of Appliances | Objective of Scheme |
---|---|---|---|---|---|

1 | [63] | GA, BPSO, ACO | ToUP and IBR | 13 | EC minimization, PAR minimization, US maximization |

2 | [112] | PIO and BFO | CPP | 16 | EC minimization, PAR minimization, US maximization |

3 | [113] | BFO and SBA | RTP | 12 | EC minimization, PAR minimization, US maximization |

4 | [114] | FPA | RTP | 16 | EC minimization, PAR minimization, US maximization |

5 | [115] | GWO and BFO | TOU | 6 | EC minimization, PAR minimization, US maximization |

6 | [116] | HSA, FA, and BFO | TOU | 15 | EC minimization, PAR minimization, US maximization |

7 | [117] | GA and CSA | RTP | 6 | EC minimization, PAR minimization, US maximization |

8 | [118] | SBA and EDE | RTP | 16 | EC minimization, PAR minimization, US maximization |

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**MDPI and ACS Style**

Shewale, A.; Mokhade, A.; Funde, N.; Bokde, N.D.
An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem. *Energies* **2020**, *13*, 4266.
https://doi.org/10.3390/en13164266

**AMA Style**

Shewale A, Mokhade A, Funde N, Bokde ND.
An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem. *Energies*. 2020; 13(16):4266.
https://doi.org/10.3390/en13164266

**Chicago/Turabian Style**

Shewale, Amit, Anil Mokhade, Nitesh Funde, and Neeraj Dhanraj Bokde.
2020. "An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem" *Energies* 13, no. 16: 4266.
https://doi.org/10.3390/en13164266