Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response
2. Requirements for Demand Response Participation in Markets
2.1. Small Customers and Aggregation
2.2. Characteristics of the Customers: End-Uses
2.3. Physically Based Load Modelling (PBLM) for End-Uses
- Dwelling/environment submodels (indoor, environment): parameters that represent heat losses/gains (conduction/convection through walls: ha, aw; the floor: arg; windows, (ag), ventilation losses/gains (HV); as well as heat gains: solar radiation (Hsw, Hw); internal gains due to inhabitants (Hr) or appliances (H(a). Also, the model takes into account heat storage from the specific heat of external walls (Cw), indoor mass (C(a) or roof/ground (Crg).
- Energy conversion submodel (the appliance): electrical energy conversion into heat (space heating), “cold” (air conditioning), or hot water. This is represented by a current source (Hch) and is independent of the dwelling submodels, see Figure 5a,b, where the same dwelling model can “host” different appliances with the same or similar service (heating/cooling).
3. Demand Response to Price (PDR): Energy Markets
3.1. Evaluation of PDR: An Economic Model to Evaluate the Size of Demand Packages
3.2. Linkage between PDR Economic Model and PBLM
4. Demand Response to System Events (EDR): Capacity Markets
- Typical time period for events: time of the day (and season) considered as peak periods by ISO. For example, some ISOs (e.g., NE-ISO) define peak periods on summer and winter weekdays, whereas other ISOs (e.g., PJM) focused only on summer peak periods. The future trend will be to consider all the seasons and broader peak periods.
- The types of DR&EE policies that can participate: almost any policy that generates savings at the time of interest for the ISO. According to some ISO manuals , some policies do not meet the CM definition if new devices do not improve present baselines, the demand is reduced by a change of behaviour, or the user switches an appliance or process from electricity to gas.
- The operational lifetime of DR&EE policies (in markets): This item is a cumbersome for customer participation in CM, because the future income largely depends on the decision whether new investments in EE and DR are engaged or not. The framework is quite different in each specific market. From four years in PJM to twenty in other markets.
- The aggregation of demand: A minimum resource size is usually required in markets. In the UK, the minimum proposed size is 2 MW whereas US markets usually allow a minimum size of 100 kW for bidding.
- Resource qualification: the sponsors of DR&EE projects should submit documentation to ISO to justify the policies being used for energy and demand savings. The DR/EE “supplier” must demonstrate that their resource is reliable and will accomplish savings at the times considered as critical periods by ISO. For these M&V plans, it is necessary to know a customer baseline and then to propose a method that involves the analysis of the impact of a measure.
- Credit requirements, payments and penalties: resources cleared in the market are paid at the clearing price for the year in question. The pacer of payments can be monthly or weekly. In some representative US markets, CM prices per kW and month are around $4/kW-year.
4.1. Evaluation of DR&EE in CM: An Economic Model to Evaluate and Build Demand Packages
5. Results and Discussion
5.1. Effects of Elasticity: Feedback from PBLM Simulation
5.2. Effects of Elasticity: PRD Simulation
5.3. Assignation of an Economic Value to DR&EE in Energy Offer Curves
Conflicts of Interest
- Directive of the European Parliament and of the Council on Common Rules for the Internal Market in Electricity (Recast). COM (2016) 864 Final/2. Available online: https://ec.europa.eu/energy/sites/ener/files/documents/1_en_act_part1_v7_864.pdf (accessed on 31 December 2017).
- Arias, M. A modern Energy Union for competitive, secure and sustainable energy for the European Industry. In Proceedings of the 2015 Euro Economic Congress, Katowice, Poland, 20–22 April 2015. [Google Scholar]
- Faruqui, A.; Sergici, S. Household response to dynamic pricing of electricity: A survey of 15 experiments. J. Regul. Econ. 2010, 38, 193–225. [Google Scholar] [CrossRef]
- PJM. Demand Response. Available online: http://www.pjm.com/markets-and-operations (accessed on 28 August 2017).
- Smart Energy Demand Coalition (SEDC). Explicit Demand Response in Europe. Mapping the Markets. Available online: http://www.smarten.eu/ (accessed on 19 December 2017).
- Energy Networks Association (UK). Smart Demand Response: A Discussion Paper. Available online: http://www.energynetworks.org/news/publications/reports.html (accessed on 29 January 2018).
- ENTSOE. Market Design for Demand Side Response. Policy Paper. November 2015. Available online: https://www.entsoe.eu/Documents/Publications (accessed on 19 December 2017).
- Smart Grid Investment Grant Program. Progress Report II. 2013. Available online: https://www.smartgrid.gov/recovery_act/overview/smart_grid_investment_grant_program.html (accessed on 29 January 2018).
- Heiskanen, E.; Hyvönen, K.; Laakso, S.; Laitila, P.; Matschoss, K.; Mikkonen, I. Adoption and Use of Low-Carbon Technologies: Lessons from 100 Finnish Pilot Studies, Field Experiments and Demonstrations. Sustainability 2017, 9, 847. [Google Scholar] [CrossRef]
- Denholm, P.; O’Connell, M.; Brinkman, G.; Jorgenson, J. Overgeneration from Solar Energy in California: A Field Guide to the Duck Chart, NREL Report. NREL/TP-6A20-65023; 2015. Available online: http://www.nrel.gov/publications (accessed on 19 December 2017).
- Álvarez, C.; Moreno, J.I.; López, G.; Carrillo, C.; Ramírez, I.J.; Matanza, J.; Valero-Verdu, S.; Gabaldón, A.; Ruiz, M. Methodologies and proposals to facilitate the integration of small and medium consumers in smart grids. CIRED Open Access Proc. J. 2017, 2017, 1895–1898. [Google Scholar] [CrossRef]
- Council of European Energy Regulators (CEER). CEER advice on Ensuring Market and Regulatory Arrangements Help Deliver Demand-Side Flexibility. Ref: C14-SDE-40-03. June 2014. Available online: http://www.ceer.eu/ (accessed on 19 December 2017).
- Chicco, G. Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy 2012, 42, 68–80. [Google Scholar] [CrossRef]
- Zeifman, M.; Roth, K. Nonintrusive Appliance Load Monitoring: Review and Outlook. IEEE Trans. Consum. Electron. 2011, 57, 76–84. [Google Scholar] [CrossRef]
- Gabaldon, A.; Molina, R.; Marin-Parra, A.; Valero-Verdu, S.; Álvarez, C. Residential End-Uses Disaggregation and Demand Response Evaluation Using Integral Transforms. J. Mod. Power Syst. Clean Energy 2017, 5, 91–104. [Google Scholar] [CrossRef]
- Gabaldon, A.; Guillamon, A.; Ruiz, M.C.; Valero, S.; Alvarez, C.; Ortiz, M.; Senabre, C. Development of a methodology for clustering electricity-price series to improve customer response initiatives. IET Gener. Trans. Distrib. 2010, 4, 706–715. [Google Scholar] [CrossRef]
- Ihara, S.; Schweppe, F.C. Physically Based Modeling of Cold Load Pickup. IEEE Power Eng. Rev. 1981, 1, 27–28. [Google Scholar] [CrossRef]
- US Department of Energy (DoE). eQUEST. The Quick Energy Simulation Tool. Available online: http://doe2.com/equest/ (accessed on 28 August 2017).
- Chatzivasileiadis, S.; Bonvini, M.; Matanza, J.; Yin, R.; Nouidui, T.S.; Kara, E.C.; Parmar, R.; Lorenzetti, D.; Wetter, M.; Kiliccote, S. Cyber physical modeling of distributed resources for distribution system operations. IEEE Proc. 2015, 104, 789–806. [Google Scholar] [CrossRef]
- Alvarez, C.; Gabaldón, A. Assessment and Simulation of the Responsive Demand Potential in End-User Facilities: Application to a University Customer. IEEE Trans. PWRS 2004, 19, 1223–1231. [Google Scholar]
- Gomes, A.; Henggeler, C.; Martinho, J. A physically-based model for simulating inverter type air conditioners/heat pumps. Energy 2013, 50, 110–119. [Google Scholar] [CrossRef]
- PJM Interconnection. Demand Response Strategy. Report. June 2017. Available online: http://www.pjm.com/~/media/library/reports-notices/demand-response/20170628-pjm-demand-response-strategy.ashx (accessed on 28 December 2017).
- Callaway, D.S. Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy. Energy Convers. Manag. 2009, 50, 1389–1400. [Google Scholar] [CrossRef]
- Perfumo, C.; Kofman, E.; Braslavsky, J.H.; Ward, J.K. Load Management: Model-based control of aggregate power for populations of thermostatically Controlled Loads. Energy Convers. Manag. 2012, 55, 36–48. [Google Scholar] [CrossRef]
- Mathieu, J.L.; Dyson, M.; Callaway, D. Using Residential Electric Loads for Fast Demand Response: The Potential Resource and Revenues, the Costs and Policy Recommendations. In Proceedings of the 2012 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, USA, 12–17 August 2012. [Google Scholar]
- Zhang, W.; Lian, J.; Chang, C.; Kalsi, K. Aggregated Modeling and Control of Air Conditioning Loads for Demand Response. IEEE Trans. Power Syst. 2013, 28, 4655–4664. [Google Scholar] [CrossRef]
- Shao, S.; Pipattanasomporn, M.; Rahman, S. Development of physical-based demand response-enabled residential load models. IEEE Trans. Power Syst. 2013, 28, 607–614. [Google Scholar] [CrossRef]
- Vrettos, E.; Koch, S.; Andersson, G. Load frequency control by aggregations of thermally stratified electric water heaters. In Proceedings of the 3rd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe), Berlin, Germany, 14–17 October 2012; pp. 1–8. [Google Scholar]
- Federal Energy Regulatory Commission (FERC) USA. “Assessment of Demand Response and Advanced Metering: Staff Report”. December 2016. Available online: https://www.ferc.gov/legal/staff-reports/2016/DR-AM-Report2016.pdf (accessed on 19 December 2017).
- Faruqui, A.; Harris, D.; Hledick, R. Unlocking the €53 Billion Savings from Smart Meters in the EU: How increasing the adoption of dynamic tariffs could make or break the EU’s smart grid investment. Energy Policy 2010, 38, 6222–6231. [Google Scholar] [CrossRef]
- KEMA Inc. PJM Empirical Analysis of Demand Response Baseline Methods; KEMA Inc.: Arnhem, The Netherlands, 2011; Available online: https://www.pjm.com/~/media/markets-ops/dsr/pjm-analysis-of-dr-baseline-methods-full-report.ashx (accessed on 31 October 2017).
- NYISO Paper. Demand Response Operations. Emergency Demand Response Program Manual. June 2016. Available online: http://www.nyiso.com/public/webdocs/markets_operations/documents/Manuals_and_Guides/Manuals/Operations/edrp_mnl.pdf (accessed on 31 October 2017).
- Ruiz, M.D.C.; Guillamón, A.; Gabaldón, A. A new approach to measure volatility in energy markets. Entropy 2012, 14, 74–91. [Google Scholar] [CrossRef]
- Ellegård, K.; Palm, J. Visualizing energy consumption activities as a tool for making everyday life more sustainable. Appl. Energy 2011, 88, 1920–1926. [Google Scholar] [CrossRef]
- Bertoldi, P.; Zancanella, P.; Boza-Kiss, B. Demand Response Status in EU Member States; Eur. 27998 EN; EU Science Hub: Brussels, Belgium, 2016. [Google Scholar] [CrossRef]
- Bertoldi, P.; López-Lorente, J.; Labanca, N. Energy Consumption and Energy Efficiency Trends in the EU-28 2000–2014; EUR 27972 EN; EU Science Hub: Ispra, Italy, 2016. [Google Scholar] [CrossRef]
- Instituto Para la Diversificación y Ahorro de la Energía (IDAE). Consumo por Usos y Energías del Sector Residencial (2010–2015). Available online: http://www.idae.es/informacion-y-publicaciones/estudios-informes-y-estadisticas (accessed on 28 August 2017).
- US Energy Information Administration. Residential Energy Consumption Survey (2015 RECS Survey Data); US Energy Information Administration: Washington, DC, USA, 2015. Available online: https://www.eia.gov/consumption/residential/data/2015/ (accessed on 19 December 2017).
- The Japan Refrigeration and Air Conditioning Industry Association. World Air Conditioner Demand by Region; The Japan Refrigeration and Air Conditioning Industry Association: Minato-ku, Tokyo, 2017. [Google Scholar]
- Demand Response Web Page. Available online: http://www.demandresponse.eu (accessed on 29 December 2017).
- Li, X.; Wen, J. Review of building energy modelling for control and operation. Renew. Sustain. Energy Rev. 2014, 37, 517–537. [Google Scholar] [CrossRef]
- Ruiz-Abellón, M.C.; Guillamón, A.; Gabaldón, A. Dependency-aware clustering of time series and its application on energy markets. Energies 2016, 9, 809. [Google Scholar] [CrossRef]
- Faruqui, A.; Sergici, S. Dynamic Pricing & Demand Response. IPU’s Annual Regulatory Studies Program. University of Michigan, 2016. Available online: http://www.brattle.com/news-and-knowledge/publications/archive/2016 (accessed on 29 December 2017).
- Labandeira, X.; Labeaga, J.M.; López-Otero, X. Estimation of Elasticity Price of Electricity with Incomplete Information. Energy Econ. 2012, 34, 627–633. [Google Scholar] [CrossRef]
- Rai, A.; Reedman, L.; Graham, P. Price and income elasticities of residential electricity demand: The Australian evidence. In Proceedings of the Australian Conference of Economists, Hobart, Australia, 1–4 July 2014. [Google Scholar]
- Bernstein, M.A.; Griffin, J. Regional Differences in the Price-Elasticity of Demand for Energy; RAND Corporation: Santa Monica, CA, USA, 2005; Available online: https://www.rand.org/pubs/technical_reports/TR292.html (accessed on 29 October 2017).
- Neenan, B.; Eom, J. Price Elasticity of Demand for Electricity: A Primer and Synthesis; Electric Power Research Institute (EPRI) Report; Electric Power Research Institute: Palo Alto, CA, USA, 2008. [Google Scholar]
- Ros, A.J. An Econometric Assessment of Electricity Demand in the United States Using Utility-Specific Panel Data and the Impact of Retail Competition on Prices. Energy J. 2017, 38, 73–99. [Google Scholar] [CrossRef]
- Fan, S.; Hyndman, R.J. The price elasticity of electricity demand in South Australia. Energy Policy 2011, 39, 3709–3719. [Google Scholar] [CrossRef]
- Burnett, C. American Electric Power 2016 Price Elasticity Study. In Proceedings of the Itron’s 15th Energy Forecasting Meeting, Chicago, IL, USA, 26–28 April 2017; Available online: http://capabilities.itron.com/efg/2017/12_ChadBurnett.pdf (accessed on 19 December 2017).
- Hosseini Imani, M.; Niknejad, P.; Barzegaran, M.R. The impact of customers’ participation level and various incentive values on implementing emergency demand response program in microgrid operation. Int. J. Electr. Power Energy Syst. 2018, 96, 114–125. [Google Scholar] [CrossRef]
- Moghaddam, M.P.; Abdollahi, A.; Rashidinejad, M. Flexible demand response programs modelling in competitive electricity markets. Appl. Energy 2011, 88, 3257–3269. [Google Scholar] [CrossRef]
- Sharifi, R.; Anvari-Moghaddam, A.; Fathi, S.H.; Guerrero, J.M.; Vahidinasab, V. Economic demand response model in liberalised electricity markets with respect to flexibility of consumers. IET Gener. Transm. Distrib. 2017, 11, 4291–4298. [Google Scholar] [CrossRef]
- Mohajeryami, S.; Moghaddam, I.; Doostan, M.; Vatani, B.; Schwarz, P. A novel economic model for price-based demand response. Electr. Power Syst. Res. 2016, 135. [Google Scholar] [CrossRef]
- Schweppe, F.C.; Caramanis, M.C.; Tabors, R.D.; Bohn, R.E. Spot Pricing of Electricity; Kluwer Ltd.: Boston, MA, USA, 1998. [Google Scholar]
- Aalami, H.A.; Parsa, M.; Yousefi, G.R. Demand response modeling considering interruptible/curtailable loads and capacity markets programs. Appl. Energy 2010, 87, 243–250. [Google Scholar] [CrossRef]
- Aalami, H.A. Khatibzadeh, A. Regulation of market clearing price based on nonlinear models of demand bidding and emergency demand response programs. COM (2015) 339 final: Communication from the EC on a New Deal for Energy Consumers. Int. Trans. Electr. Energ. Syst. 2016, 26, 2463–2478. [Google Scholar] [CrossRef]
- Kessels, K.; Kraan, C.; Karg, L.; Maggiore, S.; Valkering, P.; Laes, E. Fostering Residential Demand Response through Dynamic Pricing Schemes: A Behavioural Review of Smart Grid Pilots in Europe. Sustainability 2016, 8, 929. [Google Scholar] [CrossRef]
- Medina, A.; Cámara, A.; Monrobel, J.R. Measuring the Socieconomic and Environmental Effects of Energy Efficiency Investments for a More Sustainable Spanish Economy. Sustainability 2016, 8, 1029. [Google Scholar] [CrossRef]
- Gabaldón, A.; Alvarez, C.; Marín-Parra, A.; Guillamón, A.; Valero, S.; Senabre, C.; López, M. A Methodology for the Design of Offer Curves in the Future Capacity Markets through Energy Efficiency. In Proceedings of the EEDAL Conference, Lucerne, Switzerland, 26–28 August 2015. [Google Scholar]
- Potter, J.; Cappers, P. Demand Response Advanced Controls Framework and Assessment of Enabling Technology Costs; LBNL Report; UC Open Access Publications: Berkeley, CA, USA, 2017. [Google Scholar]
- Sustainable Energy Authority of Ireland (SEAI). Energy Efficiency Obligation Scheme-Ireland. 2014. Available online: http://www.seai.ie/EEOS/EEOS-Guidance-Document.pdf (accessed on 29 December 2017).
- General Electric. GeoSprint™ Hybrid Water Heater. Available online: http://www.geappliances.com/heat-pump-hot-water-heater/ (accessed on 29 December 2017).
- Pacific Gas and Electric Co., USA. Lighting Rebate Catalog. Available online: http://pge.com/businessrebates (accessed on 29 December 2017).
- National Action Plan for Energy Efficiency. Coordination of Energy Efficiency and Demand Response. Prepared by Charles Goldman Michael Reid (E Source), Roger Levy and Alison Silverstein. 2010. Available online: https://eetd.lbl.gov/sites/all/files/publications/report-lbnl-3044e.pdf (accessed on 29 January 2018).
- Gabarron (ELNUR SA). Manufacturers of Electrical Heating Solutions: Storage Heaters. Available online: http://www.elnur.es/productos/acumulad/iacum.htm (accessed on 29 January 2018).
|Capacity DR 1||7.5||6.5||-|
|Economic DR 1||0.9||0.6||-|
|End-Use||Thermal Energy (ktep)||Electrical Energy (ktep)||%||Proposed Flexibility of Demand|
|El. Heating (EH)||5863||448||42.9||Own/substitution|
|Water Heater (WH)||2179||454||17.9||Own/substitution|
|Lighting (LIG)||-||714||4.8||Own */NA|
|Air Conditioning (AC)||2||142||0.98||Own/substitution|
|Appliances||-||3758||25.6||(Additional details in Table 6)|
|Capacity Utilization (%)||Economic Sector (Country, Year)|
|95||DB Schenker, North Rail Freight line (Germany, 2013)|
|85||Air Berlin (Germany, 2013)|
|78||Industrial Segment, average (USA, 2013)|
|76||Manufacturing (USA, 2013)|
|64||Power System (Japan, 2009)|
|62.5||Power System (USA, 2013)|
|Authors (Country, Year)||Segment (R/C/I)||Own-Price Elasticity (Short-Run)||Own-Price Elasticity (Long-Run)||Substitution Elasticity||Source|
|Houthakker&Taylor (USA, 1970)||R||−1.13||−1.89||-|||
|Anderson (USA, 1973)||R||-||−1.12||0.30|||
|Houthakker et al. (USA, 1975)||R||−1.9||-||-|||
|Lyman (USA, 1978)||R||−1.10||-||-|||
|Bohi&Zimmerman (USA, 1984)||R||−1.2||−1.7||-|||
|Baker et al. (UK, 1989)||R||−1.79||-||0.19|||
|Beenstock et al. (Israel, 1999)||R|
|Filippini (Switzerland, 1999)||R||−1.30||-||-|||
|Filippini&Pachauri (India, 2004)||R||−1.45 (winter)|
|Hondroyiannis (Greece, 2004)||R||0||-||-|||
|Faruqui&Sergici (USA, 2003-04) 1||R||[−1.019, −1.054]||-||[0.077, 0.111]|||
|Kamerschen&Porter (USA, 2004)||R|
|Bernstein et al. (USA, 2005)||−1.24||−1.32|||
|Labandeira et al. (Spain, 2006)||R||−1.78||0.05|||
|Neenan et al. (USA, 2008)||R||−1.3||−1.9|||
|Labandeira et al. (Spain, 2010)||R|
|Fan&Hyndman (South Australia, 2011) 2||R||[−1.26, −1.51] (Winter)|
[−1.27, −1.44] (Summer)
|Rai et al. (Australia, 2014)||R||−1.447||−1.748||0.121|||
|Burnett (AEP, USA, 2016)||R||−1.08||−1.14|||
|OB||Overall Benefit (economic and load service)||(3)|
|B(Di)||Benefit of customer in time i due to demand Di||(3)|
|Di, D0i||Demand in time i (with and without DR)||(3)|
|Pi, P0i||Price in time i, peak and “usual”||(3)|
|INC, PEN||DR Incentives and penalties (if they exist in markets)||(3)|
|DSL||Demand Service Level cleared with third parties/markets||(3)|
|Eik||Demand elasticity (see Equations (1) and (2))||(4)–(9)|
|Demand of end use “eu” in time “k” during PDR||(6)–(9)|
|Description||Acronym in (12)||Cost||Revenue|
|Energy equipment and costs for any other miscellaneous items||CAP||Initial||-|
|Installing DR&EE equipment||IC||Installing||-|
|Installing a baseline equipment during the lifespan of EE measures||AIC||Avoided installing|
|Installing the load/appliance. If the old appliance reached the end of its lifespan or lifetime, the coefficient is 0, otherwise is 1.||cic||Installing coefficient|
|Operation and maintenance of DR&EE policies||OM||Operation&Maintenance|
|Adjustments in energy balance between BRP, LSE and aggregator/customers due to DR||BAL||Energy balance|
|Energy savings, losses and payback due to the application of EE&DR portfolio||ENER||∆Energy (savings)||∆Energy (payback)|
|Demand clipping due to EE or DR policies||PWR||∆Power||-|
|Revenue from utilities or governmental authorities||INC||-||Incentives subsidies|
|Revenues in markets if the offer is cleared||Cmr||Clearing price|
|N° of years that a Demand-Side policy receives the qualification into the markets (market lifetime).||myears||Operational lifetime|
|Operational lifetime of the equipment||Life||Lifespan|
|The price of electricity €/kWh (*)||price||Retail price of electricity|
|The cost of monitoring, control and communication devices||ICT||ICT costs|
|ICT equipment that can be shared by DR and EE policies in different markets||Cict (0, 1)||ICT cost coefficient|
|The debt interest rate (%)||FIN (annual)||Financial costs|
|Estimated costs associated with project design and management||AGG (annual)||Aggregator management|
|PBLM State Variable||Steady State||Preheating||DR as Usual||DR with Preheating|
|External Wall temp (Xw)||14.2–14.8||15–14.3||14.8–14.1||15–14.2|
|Indoor temp (Xi)||19.5–19.1||20.8–19.1||19.5–17.7||20.8–18.0|
|Ground-roof Temp (Xrg)||18–17.3||18.2–17.3||18–17.1||18–17.3|
|Indicator||Steady State||DR as Usual||Preheating with DR||Preheating w/o DR|
|First payback period (h)||-||0.75||0.6||-|
|Energy during payback (avg., kWh)||3.80||4.39||4.12||-|
|Operating state during control period, m(t) (%)||57.1 * (1027 W)||25 (514 W)||25 (514 W)||46.62 * (839 W)|
|Operating state during preheating, m(t) (%)||55.5||-||100||100|
|Operating state during payback, m(t) (%)||70||77.6||71.6||-|
|Daily Energy EH (kWh)||16.75||15.65||16.95||17.25|
|Policy||Offer (EH only) (kWh)||Revenue from Markets (EH) (€)||Change in Tariff Costs (EH) (€)||Energy Payback 1 (EH) (€)||Offer (Overall Demand) (kWh)||Revenue from Markets (€)|
|DR as usual||1.5||0.37||−0.3||0.1||2.1||0.45|
|DR with preheating||1.5||0.37||−0.35||0.05||2.1||0.45|
|ICT||Average of Control Technology ($/kW)||Communication and HW Costs ($/Site)|
|Energy monitoring (SM, NILM)||100–600||2080 1|
|Lighting control||220–380 1||2080 1|
|DR/EE Policy||Power Baseline (kW; kWh/day)||PWR (kW)||ENER (kWh/day)||CAP €||Price (€/kWh)||INC (€/$)|
|U factor window 1.3 (W/hm2° K)||1.29; 17.94||−0.04||−0.36||100||0.15||$20|
|LED replacement (2 lamps)||0.080; 0.09||−0.068||−0.033||10||0.15||50% 1|
|Heat Pump WH||1.2; 2.38||−0.6||−1,19||1100–1800||0.09–1.15||$500|
|EH peak clipping||1.027; 16.75||−1.502||−1.15||-||1||NA|
|EE/DR Policy||Offer Price OFP (€/kW-Year)|
|EE1: Dwelling insulation (Window U factor)||5|
|EE2: WH replacement (HPWH)||760|
|EE3: CFL replacement (LED)||<0|
|DR1: EH management||3|
|DR||Power Baseline (kW; kWh/day)||PWR (kW)||ENER (kWh/day)||CAP €||Price (€/kWh)||Myears||OFP|
|TES 3.2 kW||3.2;18.85||−1.027||+1.15||465||0.15||4||<0|
|TES 2.0 kW + HVAC 3 kcal/h||2.0; 18.85||−1.502||+1.15||342||0.15||4||<0|
|TES 0.8 kW + HVAC 3 kcal/h||1.0; 9.75||−1.502||−1.0||231 + 700||0.15||4||<0|
|EH peak clipping||1.027; 16.75||−1.502||−1.15||-||1||4||<0|
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Gabaldón, A.; Álvarez, C.; Ruiz-Abellón, M.D.C.; Guillamón, A.; Valero-Verdú, S.; Molina, R.; García-Garre, A. Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response. Sustainability 2018, 10, 483. https://doi.org/10.3390/su10020483
Gabaldón A, Álvarez C, Ruiz-Abellón MDC, Guillamón A, Valero-Verdú S, Molina R, García-Garre A. Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response. Sustainability. 2018; 10(2):483. https://doi.org/10.3390/su10020483Chicago/Turabian Style
Gabaldón, Antonio, Carlos Álvarez, María Del Carmen Ruiz-Abellón, Antonio Guillamón, Sergio Valero-Verdú, Roque Molina, and Ana García-Garre. 2018. "Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response" Sustainability 10, no. 2: 483. https://doi.org/10.3390/su10020483