Grid-Forming Virtual Power Plants: History
Please note this is an old version of this entry, which may differ significantly from the current revision.

Virtual Power Plants (VPPs) are efficient structures for attracting private investment, increasing the penetration of renewable energy and reducing the cost of electricity for consumers. It is expected that the number of VPPs will increase rapidly as their financial return is attractive to investors. VPPs will provide added value to consumers, to power systems and to electricity markets by contributing to different services such as the energy and load-following services. Grid forming inverters (GFM) are used to connect distributed energy sources and devices into a unique resource and convert it to Alternating Current. GFMs are especially suited to connecting VPPs to the main power grid.

  • virtual power plant
  • grid-forming inverters
  • renewable energy

1. Introduction

Global warming has reached a point where it is causing serious climate change, and this has focused the world’s attention on reducing the use of fossil fuel-based sources to generate electrical power [1]. In addition to this, fossil fuels are being seriously depleted, and some believe that they should be preserved for high-value applications and future generations. As a result, the use of renewable energy sources (RESs) such as solar and wind has experienced a sharp increase in recent years. Initially, large-scale RESs were integrated into distribution networks by utilities with different objectives [2][3]. For example, the authors of [4] advocated RESs to enhance the power quality of distribution networks. Likewise, reliability enhancement was another application of the integration of RESs into the power distribution grid [5]. From this, it is clear that RESs not only decrease the considerable amount of pollution derived from power generation, but also bring advantages such as loss minimization and reliability improvement.
Similarly, consumers have attempted to reduce their electricity costs and cut their CO2 emissions by installing photovoltaic (PV) panels on their house roofs [6]. Although this mechanism often helps to decrease their electricity bills, there remain some unresolved issues. Firstly, their additional generation may be partially wasted because they cannot participate effectively in the wholesale electricity market, because their generation is not significant enough to allow them to have an active role in the market. Another problem of such standalone systems is that they experience more power outages due to natural disasters, like hurricanes, earthquakes and storms. In order to tackle these problems related to rooftop PV, the concept of a virtual power plant (VPP) has been introduced [7]. Generally speaking, VPP means combining dispersed small-scale generators to create a unified system, resulting in increasing their ability to participate in the wholesale electricity market as well as increasing the stability of the grid. VPPs can be easily formed based on existing systems and they can decrease public expenditure on additional transmission lines.

2. Virtual Power Plants (VPPs)

VPPs are becoming appealing at the present time due to combining dispersed energy resources into a unified power plant which can supply the local demand as well as connecting to the main grids [8]. This section investigates the literature related to case studies of virtual power plants (VPPs) and the costing formulation of VPPs, along with the different approaches to bidding strategies for VPPs. After reviewing the case studies on VPPs, the aspects of customer engagement, demand response and gamification are discussed. Then, the components of expenses and revenues of a VPP when participating in an electricity market are presented, followed by a review of different bidding strategy algorithms for VPPs in the electricity market.

2.1. General Overview

Reducing the carbon footprint and improving the sustainability of energy systems are some of the main goals of many countries. To achieve these goals, many nations are planning for increasing renewable energy integration, for which they have set some targets, such as the contribution of 23.5% renewable generation by 2020 in Australia, which it has already achieved [9]. To speed up renewable integration, the governments provide some level of incentives to investors and end-users for the installation and use of renewable-based energy resources, such as photovoltaics (PV) and wind, and also for energy storage [10].
Considering the increase in electricity prices in Australia by 200% during the last decade, which brings financial difficulties to many people, it is critical that the use of renewable energy resources and energy storage reduces the cost of electricity for people. Energy aggregators such as VPPs have a great potential to achieve this goal of reduced electricity prices for end-users. VPPs can integrate and coordinate all available energy resources and load flexibility in one place to harmonize the use of energy in order to reduce the cost of electricity by proper planning of energy usage, electricity market participation and customer engagement [11][12].
To realize all the potential benefits of a VPP, the VPP should be carefully designed and should be managed optimally to be able to produce a profit for the VPP owner and reduce the cost of electricity for the customers [12][13]. In this research, the previous relevant works and research on VPPs, customer engagement and electricity market participation are discussed, as shown in Figure 1 [14].
Figure 1. The components of the literature review in this research.

2.2. VPP Case Studies

There are many studies that investigate different aspects of VPPs. For example, [15] evaluates the role of VPPs in encouraging the customers within the VPP to use energy efficient appliances. This study on a 63 MW VPP shows a significant energy reduction of 273 GWh/year when high-efficiency devices are installed [15].
The affordability and technical aspects of VPP implementation through multiple revenue streams are discussed for a university campus in [16]. This research shows that a VPP within a university can be a successful business case in urban areas for the owner; however, the detailed formulation of the expenses is not provided. A VPP has the ability to coordinate the flexibilities from loads for a larger gain in providing services to an electricity market or grid. The Next Kraftwerke is an example of this capability of VPPs; customers, regardless of their locations, can sign up to participate in this VPP, commit to the program and share the benefits generated by the VPP [17]. Also, a VPP can aggregate specific devices such as micro combined heat and power (CHP) modules. For example, in Germany, 25 CHPs are integrated and the effectiveness of that for Germany is investigated in [18].
There are several categories of VPPs, including community VPP or commercial VPP. Community VPP generally refers to a coordination of neighbouring residential customers and some community service utilities such as parks and aged care facilities. Practical cases of community VPP exist in Ireland, Belgium and the Netherlands [19]. Commercial VPP, on the other hand, coordinates commercial and industrial customers, for example, in a large shopping centre or in an industrial park. A case study of a commercial VPP in Scotland shows that more than a 10% increase in the VPP profit has been achieved by good management of renewables and interaction with the electricity market [20].
VPPs can be used to maximise the self-supply, such as the case in Spain, in which the VPP is designed to be as self-sufficient as possible while contributing to the electricity market and local grids [21]. In some VPP cases, some economic metric such as gross domestic product (GDP) per capita, unemployment rate, etc., are taken into account to evaluate the benefits of establishing VPPs on different aspects of social, economic and environmental criteria [22]. It is observed, for example, that a high level of renewable integration can significantly influence the prices in the electricity market [23]. A case study of VPP participation in the electricity market in Germany shows a good economic outcome [24]. For example, the revenue of the VPP increased by 11% [24]. Also, short-term techno-economic analysis of VPPs has been conducted to evaluate the effect of load dynamics, which cannot be used for the cost–benefit assessment of a VPP [25]. Islanded VPPs which are not connected to the grid are also considered as an option for renewable integration but their effectiveness is limited as they do not participate in the electricity market [26].
A summary of the comparison of literature topics is provided in Table 1.
Table 1. A comparison of recent papers on VPP case studies. (√: considered, ×: not considered, and #: reference number).
The Australian Energy Market Operator (AEMO), along with other Australian government bodies, introduced the VPP demonstration in 2019. The aim of this demonstration is to study the capabilities, capacities and challenges for the implementation of VPPs in Australia. Some of the main capabilities that AEMO intends to investigate in the VPP trials are [27]:
  • Participation in the electricity market to provide different services such as energy and ancillary services such as frequency regulation and grid voltage control;
  • Provision of operational visibility for better understanding of VPPs’ benefits;
  • Enhancement of customer satisfaction and experience;
  • Evaluation of the requirement of cyber security.
The total size of all VPPs adds up to 31 MW, and PVs and batteries are mainly involved as the participants in these trials [27].

2.3. Customer Engagement in VPPs

Customers can be engaged with VPPs in different ways such as controlling their consumption, via air conditioners, hot water systems, pool pumps, smart appliances or actively managing their energy production from solar rooftop PV systems and energy storage. These contributions are usually categorised as demand response (DR), or more generally, customer engagement. To realise these contributions, an active arrangement between customers and the VPP owner should be in place [30]. This arrangement will enable the VPP to intervene in energy-related behaviour of customers in order to change it to the required model. One of the effective platforms is smart energy management systems (EMS), which can be configured for engaging customers. EMS includes smart meters, communication, control system, user-friendly dashboard, and the appropriate hardware and software. The inclusion of game design elements into EMS provides a great opportunity for energy-related behaviour change. The gamification approach facilitates active participation and engagement of consumers with the identified values of a VPP [31].
Demand response (sometimes called load profile shaping) contributes flexibility. VPPs can use this flexibility to participate in the day-ahead, balancing or ancillary service market in order to maximise their benefit, resulting in reducing the cost of electricity for consumers [32]. In a VPP with different sources of energy, both renewable and conventional, and controllable loads, one of the main objectives is to introduce a framework that optimises the response to demand from different signals such as electricity prices, PV generation, temperature, etc. The demand response is generally categorised in two forms as follows [33]: (a) load curtailment/turn on and (b) load shift.
In the case of load curtailment/turn on, a load can be switched on or off without any requirement for utilising that load again during the timeframe. For example, a pool pump can be turned off for the whole day due to a rise in electricity price. An example of load shift is a washing machine, for which the user of this appliance can shift the use to the defined interval. In addition, considering different types of appliances, the time interval of DR is different. This is usually defined as short-term interval DR or long-term interval DR.
For the short-term interval DR, appliances such as electric water heaters (EWH) and air conditioners (AC) are considered to contribute. These or similar appliances can be turned off for a maximum of 10 to 20 min, considering the comfort level of the customer, temperature and all settings from consumers. Where these appliances are interrupted, they will not receive any signal for DR for the next period of time, depending on the settings of consumers, which could be 10 min to 1 h [34][35].
Long-term interval DR is associated with DR intervals of several hours, for example, 2 h. Appliances such as dishwashers, washing machines, pool pumps, driers, and electric vehicles (EVs) can be programmed to fit into this scheme. Customers will set their requirements for each of these appliances to make sure that their comfort levels are met. For example, they can put constraints on washing machines and dishwashers that the washing cycle should be finished before applying the DR command [36][37].
In order to facilitate the decision for customers about whether to participate in DR and nominate one or more of their appliances, a framework is necessary to consider the uncertainties in the load, PV, electricity market, cost of load curtailment or shifts for them and time-of-use tariffs (TOUs) [38]. Also, the variation of the load profile of customers should be studied by the VPP owner to make sure it satisfies the VPP’s constraints. Some DR loads can be nominated to participate in the electricity market, and some DRs contribute to congestion management of local electric grids. In this case, the grid operator can send a command for controlling these types of loads to stay below the thermal limit of equipment or grid voltage violations [39]. Flexible loads can contribute to the operation of VPPs. For example, a VPP with 46% flexible loads was studied to evaluate the effectiveness of DR programs and the comfort levels of the residents [40]. Such load flexibilities from VPPs can contribute to the reduction of the peak load and the investment in poles and wires in distribution networks [41].
In some cases, customers use combined heat and power generation in order to generate power and heat from the recovery process simultaneously. This technology can be utilised in DR programs to adjust the electricity and heat load together, which can reduce the risk of participation in DR schemes for VPPs if programmed effectively [12]. A VPP that includes a CHP should optimise the heat storage and boiler operation in order to effectively participate in a DR program, and then a scheduling decision is provided for participating in the WEM.
Although demand responses are considered as one of the sources of flexibilities for VPPs, which potentially could improve the profit of the VPP [42][43], the problem with traditional DR programs is that they are less effective due to the significant administrative burden or due to violating the comfort level of customers. Gamified DR, on the other hand, will provide a platform for customers to engage in DR programs in enjoyable ways while keeping the level of comfort that they desire.

2.4. Gamification

Games are one of the ancient ways of effective learning. People not only can learn through games but also can enjoy the whole process, resulting in the desirable behaviour change for the designed purpose. For effective customer engagement, a behavioural change associated with the use of energy needs to happen in which customers can willingly react and accept the DR commands from the VPP owner. Also, the customers can reject or accept any types of participation in DR programs or program EMS through auto-response to the desirable events. The value created by the gamified DR programs will contribute to the electricity price reduction for the customers, to VPP profit increase and to some services to the WEM and grid.
There are some energy-related applications that work based on gamified approaches. The applications that are reviewed in this research are Ecogator, Social Power Game, Makahiki, Power House, Less Energy Empowers You (LEY), Wattsup, enCOMPASS and Funergy [44]. These applications can also be used for energy efficiency/saving if programmed properly [31].
The EcoGator application can provide energy efficiency advice when a person wants to buy appliances. Also, it can compare two appliances and give insights to the customer about the sustainability of products [44]. The app will give the users some points and increase their level of involvement the more they use the functions of the application. Users of the app can also receive and share energy efficiency tips [45].
Social Power Game is designed to provide a collaborative platform amongst neighbours so that they can participate in teams for completing a task while receiving points for doing so. This constructive competition amongst teams of neighbours will result in awareness improvement related to energy consumption and realistic energy savings for households while they enjoy the social interaction between neighbours [44].
Makahiki is an application for programming any type of gaming platform. For example, a sequence of activities and actions can be designed for evaluating the energy consumption at home or evaluating the DR events to encourage faster and more accurate decision making. The players can earn points by doing certain actions. The app can provide data visualisation on energy consumption and other data [46].
The Power House application can read the energy consumption of a home and use it for giving rewards to each user in an online environment. In this platform, neighbours can enhance their social reputation by adjusting their energy-related behaviours [47].
Less Energy Empowers You (LEY) is a gaming platform that challenges the users by encouraging them to use energy optimally in order to obtain maximum points. It also provides some quizzes that award additional points if completed by the user [48].
Wattsup is a Facebook-based application which provides ranking, rewards and comparison amongst friends on the basis of their energy consumption [49].
The enCompass and Funergy are other gamification platforms for energy saving which utilise data collection sensors including user data, data analytics, action recommendations, and a programmable gamification system [50]. The collected data can be compared with some reference data or other users’ data to provide some insights to the users about their levels on the ladder and to give some recommendations on the appropriate use of energy [44][51].

3. Concept of a Grid-Forming VPP

Grid-forming inverters are used to connect dispersed resources into a unique resource and convert it to Alternating Current (AC). The design of a grid-forming inverter is illustrated in Figure 2 [52]. As can be seen, the PV modules generate direct current power by receiving energy from the Sun. The generated power may have a low/high level of voltage which can be controlled by DC-DC inverters [53]. Then, the regulated voltage from the DC-DC inverters is connected to DC-AC inverters in a DC link [54]. Eventually, the AC power is transmitted to the power grid by step-up transformers. This GFM inverter enables us to simultaneously convert DC to AC and inject/absorb reactive power into the network.
Figure 2. Schematic of grid-forming inverters.
The above diagram, for a grid-forming inverter, can be extended to develop a grid-forming VPP. A schematic is illustrated in Figure 3. As can be seen from this procedure, houses are equipped with solar panels. The generation of these panels is connected to the VPP control center by means of a grid-forming inverter. Inverters convert the DC power generated by the PV sources into AC form. The AC power is used to supply consumers, and extra power is sold to the main grid. It should be noted that the GFM inverter can also absorb/inject reactive power into the network.
Figure 3. Schematic of a grid-forming VPP without energy storage.
It is notable that PVs have intermittent generation, meaning that they only generate over specific times, for roughly 8–18 h each day. Accordingly, energy storage systems can also be integrated into the network in order to increase the flexibility and stability of the VPP. Figure 4 illustrates a grid-forming VPP in the presence of an energy storage system. This is an example of a clean VPP. It should be noted that diesel generators could also be integrated to increase the flexibility and reliability of the VPP. However, they produce greenhouse emissions, so their use should be minimised and they should only be used as a backup source for emergency situations.
Figure 4. Schematic of a grid-forming VPP with energy storage.
It is worth noting that recently, grid-forming inverters have become available in various sizes and could easily be used to create a grid-forming VPP. For example, Tesla has provided utility-scale inverters which can facilitate the design of grid-forming VPPs.

This entry is adapted from the peer-reviewed paper 10.3390/en15239049

References

  1. Olabi, A.; Abdelkareem, M.A. Renewable energy and climate change. Renew. Sustain. Energy Rev. 2022, 158, 112111.
  2. Wang, W.; Huang, Y.; Yang, M.; Chen, C.; Zhang, Y.; Xu, X. Renewable energy sources planning considering approximate dynamic network reconfiguration and nonlinear correlations of uncertainties in distribution network. Int. J. Electr. Power Energy Syst. 2022, 139, 107791.
  3. Mozafar, M.R.; Moradi, M.H.; Amini, M.H. A simultaneous approach for optimal allocation of renewable energy sources and electric vehicle charging stations in smart grids based on improved GA-PSO algorithm. Sustain. Cities Soc. 2017, 32, 627–637.
  4. Jamil, E.; Hameed, S.; Jamil, B.; Qurratulain. Power quality improvement of distribution system with photovoltaic and permanent magnet synchronous generator based renewable energy farm using static synchronous compensator. Sustain. Energy Technol. Assess. 2019, 35, 98–116.
  5. Jafari, A.; Khalili, T.; Ganjehlou, H.G.; Bidram, A. Optimal integration of renewable energy sources, diesel generators, and demand response program from pollution, financial, and reliability viewpoints: A multi-objective approach. J. Clean. Prod. 2019, 247, 119100.
  6. Malekpour, A.R.; Pahwa, A. A Dynamic Operational Scheme for Residential PV Smart Inverters. IEEE Trans. Smart Grid 2016, 8, 2258–2267.
  7. Vahedipour-Dahraie, M.; Rashidizadeh-Kermani, H.; Shafie-Khah, M.; Catalao, J.P.S. Risk-Averse Optimal Energy and Reserve Scheduling for Virtual Power Plants Incorporating Demand Response Programs. IEEE Trans. Smart Grid 2021, 12, 1405–1415.
  8. Sikorski, T.; Jasiński, M.; Ropuszyńska-Surma, E.; Węglarz, M.; Kaczorowska, D.; Kostyla, P.; Leonowicz, Z.; Lis, R.; Rezmer, J.; Rojewski, W.; et al. A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept: Technical Aspects. Energies 2020, 13, 3086.
  9. CER 2020 Large-Scale Renewable Energy Target Capacity Achieved. Available online: http://www.cleanenergyregulator.gov.au/RET/Pages/News and updates/NewsItem.aspx?ListId=19b4efbb-6f5d-4637-94c4-121c1f96fcfe&ItemId=683 (accessed on 4 September 2019).
  10. COAG Renewable Energy Target Scheme Design. Available online: https://www.coag.gov.au/node/219 (accessed on 29 June 2022).
  11. Dulau, L.I.; Abrudean, M.; Bica, D. Distributed Generation and Virtual Power Plants. In Proceedings of the Universities Power Engineering Conference; IEEE Computer Society, Cluj-Napoca, Romania, 2–5 September 2014.
  12. Ghavidel, S.; Li, L.; Aghaei, J.; Yu, T.; Zhu, J. A Review on the Virtual Power Plant: Components and Operation Systems. In Proceedings of the 2016 IEEE International Conference on Power System Technology, Powercon 2016, Wollongong, NSW, Australia, 28 September–1 October 2016; pp. 1–6.
  13. Abdolrasol, M.G.M.; Hannan, M.A.; Mohamed, A.; Amiruldin, U.A.U.; Abidin, I.B.Z.; Uddin, M.N. An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration Using the Binary Backtracking Search Algorithm. IEEE Trans. Ind. Appl. 2018, 54, 2834–2844.
  14. Behi, B.; Jennings, P.; Arefi, A.; Pivrikas, A. Bidding Strategy for a Virtual Power Plant for Trading Energy in the Wholesale Electricity Market-Murdoch University Research Repository. Available online: https://researchrepository.murdoch.edu.au/id/eprint/65657/ (accessed on 21 November 2022).
  15. Li, Y.; Gao, W.; Ruan, Y. Feasibility of virtual power plants (VPPs) and its efficiency assessment through benefiting both the supply and demand sides in Chongming country, China. Sustain. Cities Soc. 2017, 35, 544–551.
  16. Wang, H.; Riaz, S.; Mancarella, P. Integrated techno-economic modeling, flexibility analysis, and business case assessment of an urban virtual power plant with multi-market co-optimization. Appl. Energy 2019, 259, 114142.
  17. Lehmbruck, L.; Kretz, J.; Aengenvoort, J.; Sioshansi, F. Aggregation of Front-and behind-the-Meter: The Evolving VPP Business Model. In Behind and beyond the Meter: Digitalization, Aggregation, Optimization, Monetization; Sioshansi, F., Ed.; Academic Press: Cambridge, MA, USA, 2020; pp. 211–232. ISBN 9780128199510.
  18. German virtual power plant project using 25 BlueGen mCHP units. Fuel Cells Bull. 2012, 2012, 4.
  19. van Summeren, L.F.; Wieczorek, A.J.; Bombaerts, G.J.; Verbong, G.P. Community energy meets smart grids: Reviewing goals, structure, and roles in Virtual Power Plants in Ireland, Belgium and the Netherlands. Energy Res. Soc. Sci. 2019, 63, 101415.
  20. Elgamal, A.H.; Kocher-Oberlehner, G.; Robu, V.; Andoni, M. Optimization of a multiple-scale renewable energy-based virtual power plant in the UK. Appl. Energy 2019, 256, 113973.
  21. Dietrich, K.; Latorre-Canteli, J.M.; Olmos, L.; Ramos, A. Modelling and assessing the impacts of self-supply and market-revenue driven Virtual Power Plants. Electr. Power Syst. Res. 2015, 119, 462–470.
  22. Parag, Y.; Ainspan, M. Sustainable microgrids: Economic, environmental and social costs and benefits of microgrid deployment. Energy Sustain. Dev. 2019, 52, 72–81.
  23. Moreno, B.; Díaz, G. The impact of virtual power plant technology composition on wholesale electricity prices: A comparative study of some European Union electricity markets. Renew. Sustain. Energy Rev. 2018, 99, 100–108.
  24. Loßner, M.; Böttger, D.; Bruckner, T. Economic assessment of virtual power plants in the German energy market—A scenario-based and model-supported analysis. Energy Econ. 2017, 62, 125–138.
  25. Magdy, F.E.Z.; Ibrahim, D.K.; SABRY, W. Virtual Power Plants Modeling and Simulation using Innovative Electro-Economical Concept. In Proceedings of the 2019 16th Conference on Electrical Machines, Drives and Power Systems (ELMA), Varna, Bulgaria, 6–8 June 2019; pp. 1–5.
  26. Veilleux, G.; Potisat, T.; Pezim, D.; Ribback, C.; Ling, J.; Krysztofiński, A.; Ahmed, A.; Papenheim, J.; Pineda, A.M.; Sembian, S.; et al. Techno-economic analysis of microgrid projects for rural electrification: A systematic approach to the redesign of Koh Jik off-grid case study. Energy Sustain. Dev. 2019, 54, 1–13.
  27. AEMO Virtual Power Plant (VPP) Demonstrations. Available online: https://aemo.com.au/initiatives/major-programs/nem-distributed-energy-resources-der-program/pilots-and-trials/virtual-power-plant-vpp-demonstrations (accessed on 1 February 2022).
  28. Synergy Schools VPP Pilot Project. Available online: https://www.synergy.net.au/Our-energy/For-tomorrow/Schools-VPP-Pilot-Project (accessed on 1 February 2022).
  29. Synergy Project Symphony. Available online: https://www.synergy.net.au/Our-energy/For-tomorrow/Project-Symphony (accessed on 30 September 2022).
  30. AEMO Data Dashboard for Live Market Prices. Available online: https://www.aemo.com.au/Electricity/Wholesale-Electricity-Market-WEM/Data-dashboard(live) (accessed on 24 November 2022).
  31. AlSkaif, T.; Lampropoulos, I.; Broek, M.V.D.; van Sark, W. Gamification-based framework for engagement of residential customers in energy applications. Energy Res. Soc. Sci. 2018, 44, 187–195.
  32. Hadayeghparast, S.; Farsangi, A.S.; Shayanfar, H. Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant. Energy 2019, 172, 630–646.
  33. Al-Awami, A.T.; Amleh, N.A.; Muqbel, A.M. Optimal Demand Response Bidding and Pricing Mechanism with Fuzzy Optimization: Application for a Virtual Power Plant. IEEE Trans. Ind. Appl. 2017, 53, 5051–5061.
  34. Farsangi, A.S.; Hadayeghparast, S.; Mehdinejad, M.; Shayanfar, H. A novel stochastic energy management of a microgrid with various types of distributed energy resources in presence of demand response programs. Energy 2018, 160, 257–274.
  35. Raoofat, M.; Saad, M.; Lefebvre, S.; Asber, D.; Mehrjedri, H.; Lenoir, L. Wind power smoothing using demand response of electric vehicles. Int. J. Electr. Power Energy Syst. 2018, 99, 164–174.
  36. Nguyen, H.T.; Le, L.B.; Wang, Z. A Bidding Strategy for Virtual Power Plants with the Intraday Demand Response Exchange Market Using the Stochastic Programming. IEEE Trans. Ind. Appl. 2018, 54, 3044–3055.
  37. Tang, W.-J.; Yang, H.-T. Optimal Operation and Bidding Strategy of a Virtual Power Plant Integrated with Energy Storage Systems and Elasticity Demand Response. IEEE Access 2019, 7, 79798–79809.
  38. Dabbagh, S.R.; Sheikh-El-Eslami, M.K. Participation of Demand Response Resources through Virtual Power Plant: A Decision Framework under Uncertainty. In Proceedings of the 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC), Rome, Italy, 10–13 June 2015; pp. 2045–2049.
  39. Nosratabadi, S.M.; Hooshmand, R.-A.; Gholipour, E. Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy. Appl. Energy 2016, 164, 590–606.
  40. Royapoor, M.; Pazhoohesh, M.; Davison, P.J.; Patsios, C.; Walker, S. Building as a virtual power plant, magnitude and persistence of deferrable loads and human comfort implications. Energy Build. 2020, 213, 109794.
  41. Arefi, A.; Abeygunawardana, A.; Ledwich, G. A New Risk-Managed Planning of Electric Distribution Network Incorporating Customer Engagement and Temporary Solutions. IEEE Trans. Sustain. Energy 2016, 7, 1646–1661.
  42. Liang, H.; Ma, J. Data-Driven Resource Planning for Virtual Power Plant Integrating Demand Response Customer Selection and Storage. IEEE Trans. Ind. Inform. 2021, 18, 1833–1844.
  43. Wang, Y.; Ai, X.; Tan, Z.; Yan, L.; Liu, S. Interactive Dispatch Modes and Bidding Strategy of Multiple Virtual Power Plants Based on Demand Response and Game Theory. IEEE Trans. Smart Grid 2015, 7, 510–519.
  44. Albertarelli, S.; Fraternali, P.; Herrera, S.; Melenhorst, M.; Novak, J.; Pasini, C.; Rizzoli, A.-E.; Rottondi, C. A Survey on the Design of Gamified Systems for Energy and Water Sustainability. Games 2018, 9, 38.
  45. Peham, G.; Michalczuk, R.M.B. The ecoGator App: Gamification for Enhanced Energy Efficiency in Europe. In Proceedings of the Second International Conference on Technological Ecosystems for Enhancing Multiculturality, Salamanca, Spain, 1 October 2014; ACM: New York, NY, USA; pp. 179–183.
  46. Lee, Y.; Brewer, R.S.; Johnson, P.M. Makahiki: An Open Source Game Engine for Energy Education and Conservation; Department of Information and Computer Sciences, University of Hawaii: Honolulu, HI, USA, 2012.
  47. Reeves, B.; Cummings, J.J.; Scarborough, J.K.; Flora, J.; Anderson, D. Leveraging the Engagement of Games to Change Energy Behavior. In Proceedings of the 2012 International Conference on Collaboration Technologies and Systems (CTS), Denver, CO, USA, 21–25 May 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 354–358.
  48. Madeira, R.N.; Silva, A.; Santos, C.; Teixeira, B.; Romão, T.; Dias, E.; Correia, N. LEY!: Persuasive Pervasive Gaming on Domestic Energy Consumption-Awareness. In Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology, Lisbon, Portugal, 8 November 2011; ACM: New York, NY, USA, 2011; p. 72.
  49. Foster, D.; Blythe, M.; Cairns, P. Wattsup?: Motivating Reductions in Domestic Energy Consumption Using Social Networks. In Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries, Reykjavik, Iceland, 16 October 2010; ACM: New York, NY, USA, 2010; pp. 178–187.
  50. Fraternali, S.; Novak, J.; Melenhorst, M.; Tzovaras, D.; Krinidis, S.; Rizzoli, A.E.; Rottondi, C.; Cellina, F. enCOMPASS—An Integrative Approach to Behavioural Change for Energy Saving. In Proceedings of the 2017 Global Internet of Things Summit (GIoTS), Geneva, Switzerland, 6 June 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–6.
  51. Fraternali, P.; Cellina, F.; Herrera, S.; Krinidis, S.; Pasini, C.; Rizzoli, A.E.; Rottondi, C.; Tzovaras, D. A Socio-Technical System Based on Gamification Towards Energy Savings. In Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Athens, Greece, 19–23 March 2018; pp. 59–64.
  52. Gawhade, P.; Ojha, A. Recent advances in synchronization techniques for grid-tied PV system: A review. Energy Rep. 2021, 7, 6581–6599.
  53. Mahery, H.M.; Babaei, E. Mathematical modeling of buck–boost dc–dc converter and investigation of converter elements on transient and steady state responses. Int. J. Electr. Power Energy Syst. 2013, 44, 949–963.
  54. Scarabelot, L.T.; Rambo, C.R.; Rampinelli, G. A relative power-based adaptive hybrid model for DC/AC average inverter efficiency of photovoltaics systems. Renew. Sustain. Energy Rev. 2018, 92, 470–477.
More
This entry is offline, you can click here to edit this entry!
Video Production Service