A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment
Abstract
:1. Introduction
- Description of various DSM strategies.
- Conduct of a comprehensive review of previous and current research works on DSM through soft computing and optimization techniques.
- Proposal of new viewpoints and challenges for further research.
2. Demand Side Management
- Reduction in generation margin;
- Improvement of the economic viability of the grid and its operating efficiency;
- Improvement of the economic viability of the distribution network;
- Maintenance of demand-supply balance with renewable;
- Increasing the efficiency of the overall energy supply system.
2.1. Energy Conservation and Energy Efficiency
- Greater use of renewable energy sources.
- Shifting towards super-critical technologies for conventional power plants.
- Energy efficient innovative measures under the overall realm of the Energy Conservation Act 2001.
2.1.1. Energy Conservation and Energy Efficiency Programs
- Standards and Labeling programs—To provide consumers with a choice regarding the energy-saving potential and thus the cost-saving potential of the related product in the market. These programs aid the vision of energy surplus India with 24 * 7 power to all [1].
- Energy Conservation Buildings Code—To set minimum energy standards for large commercial buildings having a connected load of 100 kW or contract demand of 120 KVA and above. For the residential sector, Eco-Niwas Samhita is launched to set various standards for limited heat gain and heat loss and for achieving natural ventilation and daylighting. Figure 3 shows the Eco-Niwas Samhita Scheme in the Residential sector [1].
- Strengthening Institutional Capacity of States—To set up State Designated Agencies for initiating the energy conservation activities at the state level.
- School Education Program—To promote energy efficiency in schools through the formation of Energy Clubs. BEE is realizing the Students Capacity Building Programme under the Energy Conservation awareness scheme for the XII five year plan.
- Human Resource Development—To implement energy-efficient technologies and practices in various sectors, a sound policy is required for the creation, retention, and up-gradation of skills of human resources.
- National Mission for Enhanced Energy Efficiency—One of the eight missions under the National Action Plan on Climate Change (NAPCC) is the National Mission for Enhanced Energy Efficiency (NMEEE). The goal of NMEEE is to improve energy efficiency by establishing a favorable regulatory and policy regime for encouraging innovative sustainability in energy efficiency.
2.1.2. Energy Efficiency Projects in India
- Energy Efficiency in light Bulb: Domestic Efficient Lighting Program (DELP) scheme (now renamed as Unnat Jeevan by Affordable LEDs and Appliances for All (UJALA)) is designed to monetize energy consumption reduction in the household sector and to attract investments therein. Approximately 45,865 mn kWh of energy were saved per year according to the Ministry of Power, and carbon emissions were reduced by 3, 71, 50, 810 tonnes. For the fiscal year 2019-20, nearly 40 crores of LED bulbs were distributed under UJALA Yojana, resulting in cost savings of Rs 18,341 crores per year [17].
- Energy Efficiency in Street Lighting: The inefficient sodium and mercury vapor street lights were replaced by efficient LED street lights in many cities with a payback period of nearly two years. New technologies in LED-based street lights offer noise and pollution sensors, with remote control facilities.
- Energy Efficiency in Water Pumping: Five States in the Agricultural sector and 8 States in the Municipal sector replaced the traditional pump with its energy-efficient counterpart. The profound transition towards solar energy is making the water pumping system even smarter and efficient than the previous technologies.
2.2. Demand Response
2.2.1. Price-Based DR Program
2.2.2. Incentive-Based DR Program
2.3. Energy Optimization and Scheduling
2.4. Distributed Generation
2.5. Energy Storage
3. Hardware and Communication Technology
4. Soft Computing Based DSM
4.1. FL Based DSM
4.2. ANN Based DSM
4.3. EC Based DSM
5. Optimization Based DSM
6. Miscellaneous
7. Discussion and Future Works
- As the number of HVAC systems is increasing, heat dissipation from the condensing coil is also increasing, thereby causing environmental issues indirectly affecting human comfort. To overcome the challenge there is a need for the development of a DSM scheme that can accommodate this heat which can either be used for space heating or in kitchen applications.
- The majority of the research focused on thermal, visual, and air quality comfort, but did not consider humidity, social comfort, and assisted living in their experiments.
- Design and real-time implementation of hybrid DR controllers considering both technical and economic aspects of the grid to provide enough knowledge of the system (experience) concerning decentralized control and to maintain the reliability of the grid (to control the peaks at off-peak hours).
- Integration of Fuzzy Logic with metaheuristic algorithms capable of energy prediction, optimization, and scheduling in real-time could give the best results for energy consumption minimization without affecting the degree of comfort.
- The system should also include renewable energy resources, energy storage devices, and an IoT based protocol to maintain the flexibility and security within the smart home.
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Correction Statement
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Attributes | Energy Conservation | Energy Efficiency |
---|---|---|
Meaning | Changing behavior or habits for using less energy | Using the technology that uses less energy |
User-interaction | Yes | May or may not |
Type of load | Traditional loads | Digital loads |
User comfort | Compromise | Maximum |
Examples |
|
|
S. No. | International Collaboration | Programmes |
---|---|---|
1 | Indo-US | Development of ECBC, Energy Efficient HVAC systems, Capacity Building for Institutional Financing |
2 | Indo-UK | Industrial Energy Efficiency, DSM Action Plans, Carbon Budgeting Approach |
3 | Indo-Japan | Energy Conservation Guidelines and Manuals, Waste Heat Recovery Projects, Joint Policy Researchers, Capacity Building and Industrial Energy Efficiency Programmes |
4 | Indo-German | Energy-Efficient Cooling, Energy Efficiency Standards for Multistorey Buildings, Perform, Achieve, and Trade (PAT) cycle |
5 | Indo-Switzerland | Smart GHAR Project, Energy Efficient Buildings via Integrated Design Method, Training Programmes |
Attributes | Price-Based DR | Incentive-Based DR |
---|---|---|
Price-variation | Time-dependent | Time-independent |
Requirement | Price-design | Baseline estimation |
Discounts offered | Time-varying | Fixed or Time-varying |
Consumer participation | Voluntary | Voluntary, mandatory, or market-based |
Applicability | Mostly addressed to have a propensity for less electricity use during peak hours | Mostly addressed during overload periods or emergencies |
References | Method | Objective | Contribution |
---|---|---|---|
[71,72,73] | FL | thermal, visual, and air quality comfort | Fuzzy P, Fuzzy PD, Fuzzy PID, Adaptive fuzzy PD controller, and GA tuned Fuzzy controller |
[74] | FL | thermal comfort | An adaptive fuzzy controller |
[75] | FL | visual comfort | fuzzy-based automatic roller blind |
[76,77] | FL | air quality comfort | Improved adaptive fuzzy controllers in real-time |
[78] | FL | thermal, visual, and air quality comfort | intelligent coordinator control with five fuzzy controllers |
[79,81] | FL | thermal comfort | A scheduling problem for air conditioner temperature control based on day-ahead pricing is modeled |
[83,84] | FL | thermal comfort | smart thermostat for the HVAC system using a programmable communicating thermostat and an adaptive model to adjust the user’s changing preferences |
[58] | FL | visual comfort | A fuzzy logic-based smart LED lighting system |
[86] | FL | thermal comfort | Fuzzy based controller for HVAC system using the Building Control Virtual Test Bed platform |
[87] | FL | - | A fuzzy logic-based smart HEMS for battery and load management |
[8] | FL | thermal, visual and air quality comfort | fuzzy controllers to control the HVAC and illumination system |
[90] | ANN | thermal comfort | ANN-based predictive and adaptive control logic |
[91] | ANN | thermal and air quality comfort | discrete model predictive approach is developed for an HVAC system |
[94,95,96] | ANN | thermal comfort | forecasting DR signals and energy consumption patterns for maintaining an energy-efficient smart home |
[97] | ANN | visual comfort | hybrid Lightning Search algorithm LSA-ANN-based HEMS |
[100,101] | ANN | thermal and air quality comfort | hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) controller |
[103] | EC | load scheduling | Binary Particle Swarm Optimization (BPSO) for scheduling interruptible loads for cost and interruption minimization |
[13] | EC | load scheduling | A day-ahead scheduling method using a heuristic evolutionary algorithm |
[104] | EC | economoc dispatch problem | an energy management system for micro-grids equipped with wind-turbines using ACO |
[105] | EC | load scheduling | dual pricing model RTP with Inclined Block Rate (IBR) |
[107,108,109] | EC | user comfort | GA is compared with ACO and PSO |
[113,114] | EC | load scheduling | used Artificial Bee Colony for energy management considering renewables as well |
[115,116,117] | EC | energy cost reduction | Different heuristic algorithms GA, ACO, BPSO, Wind-Driven Optimization, Bacterial Foraging Optimization, and Hybrid GA-PSO are compared |
[119] | EC | energy management | A real-time appliance scheduling is performed by Binary Backtracking Search Algorithm |
[120,121,122] | EC | electricity cost and peak load reduction | home energy management schemes comprising of GA, Cuckoo Search Algorithm, BPSO, and Crow Search Algorithm |
[123] | EC | load scheduling | An optimal energy scheduler for load reliability using PSO |
[124] | EC | load scheduling | A real-time electricity scheduler considering renewables and energy storage resources |
[125] | EC | load scheduling | A hybrid Harmony Search-PSO algorithm |
[126] | EC | visual comfort | Lightlearn controller based on reinforcement learning |
[127] | EC | load scheduling | a bi-level deep reinforcement learning approach |
[128] | EC | load scheduling | Dijkstra algorithm compared with GA, Optimal Pattern Recognition Algorithm, and BPSO |
References | Optimization Method | Objective | Contribution |
---|---|---|---|
[129,130] | Game Theory | minimize energy costs and Peak to Average Ratio (PAR) | Autonomous Game-Theory-based DSM |
[132,133,134] | Game Theory | load scheduling | realizing user-aware DSM considering user preferences and renewable sources |
[135] | Stochastic-Robust | price minimization | RTP-based DSM |
[137,138,139,140] | Mixed-integer programming | load scheduling | smart home appliance scheduling |
[143] | Simulated Annealing | energy optimization | DSM using white tariff |
[146] | Linear programming | load scheduling | A comparative study among Linear programming, PSO, Extended PSO, Adaptive dynamic programming, and Self-learning procedures |
[148] | Interval number optimization | load scheduling | BPSO coupled with Integer linear programming |
[150] | Dynamic programming | load scheduling | A heuristic optimization based energy management system considering both renewable sources as well as user preferences |
[151] | MILP | load scheduling | normalized weighted sum and compromise programming for solving scheduling problems considering the TOU pricing scheme |
[152] | Bat algorithm, Flower pollination, and hybrid Bat Flower pollination | load scheduling | Energy management scheduler for smart home |
S. No. | Soft Computing Based DSM | Optimization-Based DSM |
---|---|---|
1. | Set of computational techniques and algorithms that are used to deal with complex problems [154]. | Selection of the best possible element from several alternatives to achieve a target [31] |
2. | Does not require a mathematical model | Requires mathematical model |
3. | Approximate solutions | Accurate solutions |
4. | Fast | Time-consuming |
5. | May use heuristics or learning methods | Require iterative methods |
6. | Simplicity, adaptability, and flexibility | Robustness, stochastic, and optimality |
7. | Best suited for real-world problems | It may be difficult to solve real-world problems |
8. | Examples- Fuzzy logic [8], Artificial neural network [101], Genetic algorithm [124], Particle swarm optimization [125], Ant colony optimization [104], Cuckoo search algorithm [120], etc. | Examples- Game theory [129], Mixed-integer linear programming [151], Dynamic programming [150], Simulated annealing [143], Interval number optimization [148], Stochastic and Robust optimization [135], etc. |
References | Contribution |
---|---|
[156] | Integration of renewable energies through microcontroller based embedded system |
[157] | TOU-based DSM schemes for both prosumers and consumers and a scheduling algorithm that takes into account the customer preferences |
[160] | A naïve control method for controlling an electric water heater without a temperature parameter |
[36] | DR based off-line scheduling algorithm considering renewable sources |
[62,161] | The hardware implementation of DR programs and cloud computing methods considering customer’s preferences and load priority |
[162] | A smart residential energy management system for appliances and battery scheduling |
[163] | Efficient load scheduling implementation of thermostatic devices using Graph theory and the Fast greedy approach |
[165,166,167,168,169] | Model predictive controllers for scheduling thermostatic appliances |
[172] | DR management through practical implementation |
[173] | Energy management algorithm considering renewable power, battery state of charge level, grid availability, and different tariffs |
[174] | Residential load simulator using MATLAB-Simulink graphical user interface |
[47,64] | Hardware demonstration for DR management using Zigbee protocol |
[177] | Real-time rule-based DR controller with load shifting and curtailment mechanisms |
[66] | Hardware demonstration of DSM for controlling air conditioners through Wi-Fi technology and DR programs |
[64] | Appliance scheduling in a DR environment using the combination of fuzzy controller, rolling optimization, and real-time control strategy |
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Iqbal, S.; Sarfraz, M.; Ayyub, M.; Tariq, M.; Chakrabortty, R.K.; Ryan, M.J.; Alamri, B. A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment. Sustainability 2021, 13, 7170. https://0-doi-org.brum.beds.ac.uk/10.3390/su13137170
Iqbal S, Sarfraz M, Ayyub M, Tariq M, Chakrabortty RK, Ryan MJ, Alamri B. A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment. Sustainability. 2021; 13(13):7170. https://0-doi-org.brum.beds.ac.uk/10.3390/su13137170
Chicago/Turabian StyleIqbal, Sana, Mohammad Sarfraz, Mohammad Ayyub, Mohd Tariq, Ripon K. Chakrabortty, Michael J. Ryan, and Basem Alamri. 2021. "A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment" Sustainability 13, no. 13: 7170. https://0-doi-org.brum.beds.ac.uk/10.3390/su13137170