Technical demand response potentials of the integrated steelmaking site of Tata Steel in IJmuiden
- 621 Downloads
Power generation from intermittent renewable energy sources in northwest Europe is expected to increase significantly in the next 20 years. This reduces the predictability of electricity generation and increases the need for flexibility in electricity demand. Data on demand response (DR) capacities of electricity-intensive consumers is limited for most countries. In this paper, we evaluate the DR potential that can be provided to the Dutch national grid by the integrated steelmaking site of Tata Steel in IJmuiden (TSIJ). TSIJ generates electricity from its works arising gases (WAGs). The DR potentials are evaluated by using a linear optimisation model that calculates the optimal allocation of WAGs of TSIJ in case of a call for DR by the transmission system operator. The optimisation is done subject to the technical constraints of the WAG distribution network, WAG storage capacities, the on-site demand for WAGs and the ramp-up rate of the power plant that runs on WAGs. Results show that TSIJ can supply 10 MW for two programme time units (equal to 15-min period in the Netherlands) of positive DR capacity (demand reduction) with an availability rate of 97%. This is not sufficient for participating in the current emergency capacity programs in the Netherlands, which require at least 20 MW for longer than one programme time unit. Tata Steel can provide 20 MW DR capacity with an availability rate of 65%. The negative DR capacity (demand increase) of Tata Steel in IJmuiden is found to be 20 MW supplied for three programme time units and four programme time units with doubling of blast furnace gas storage capacities.
KeywordsDemand response Electricity emergency balancing capacity Integrated steelmaking Linear optimisation
blast furnace gas
basic oxygen furnace gas
coke oven gas
negative demand response measure
positive demand response measure
programme time units
Tata Steel in IJmuiden
transmission system operator
works arising gases
units (power plants: VN24, VN25, IJMO1)
boilers and TSIJ factories
Notation Explanation (unit)
- GHk, t
amount of gas in the gas holder k at time interval t (Nm3)
- Fu, k, t
average flow rate of WAG k to unit u at time interval (Nm3)
- Fupu, k, t
increase in WAG k flow rate to unit u as a DR measure at time interval t (Nm3/h)
- Fdownu, k, t
reduction in WAG k flow rate to unit u as a DR measure at time interval t (Nm3/h)
- Fconk, t
total consumption of WAG k at time interval t (Nm3/h)
- Fstoredk, t
average flow rate of WAG k to the corresponding gas holder at time interval t (Nm3/h)
- Egenu, t
electricity generated from power plant u at time interval t (MWh)
negative demand response capacity at time interval m (MW)
positive demand response capacity at time interval m (MW)
efficiency of unit u (%)
- Fgenk, t
amount of WAG k generated at time interval t (Nm3/h)
- Fncu, k, t
average flow rate of WAG k to unit u under normal conditions (without a DR measure) at time interval t (Nm3/h)
electricity demand of the on-site processes at time interval t (MWh)
- ck, t
average calorific value of WAG k at time interval t (MJ/Nm3)
- Fminu, k, t
minimum k gas flow rate to unit u (Nm3/h)
- Fmaxu, k, t
maximum k gas flow rate to unit u (Nm3/h)
- Fchminu, k
minimum change in k gas flow rate to unit u
- Fchmaxu, k
maximum change in k gas flow rate to unit u
minimum energy input to unit u (MJ/h)
maximum energy input to unit u (MJ/h)
minimum change in energy input to unit u ((MJ/h)/15 min)
maximum change in energy input to unit u ((MJ/h)/15 min)
- GHmink, t
minimum gas level that should be attained at gas holder k (Nm3)
- GHmaxk, t
maximum gas level that should be attained at gas holder k (Nm3)
As part of the Climate Package, adopted in 2008, the European Union has set clear goals for increasing the use of renewable energy. For 2020, a target of 20% has been set as share of total final energy demand, and, for 2030, a target of 27% is proposed (European Commission 2016). These targets are differentiated per member state and range from 10% in Malta to 49% in Sweden in 2020. The pathways to the national targets are specified in national renewable energy action plans. The expected increase in renewable energy in 2020 compared to 2005 consists for 80% of wind power and photovoltaics (JRC 2017), which are both intermittent renewable energy sources. In the Netherlands, renewable energy targets have been set of 14 and 16% of energy demand in 2020 and 2023, respectively (Rijksoverheid 2016). To reach these targets, it is agreed to increase the onshore wind capacity to 6000 MW by 2020 and offshore wind capacity to 4450 MW by 2023 (Ministerie van Economische Zaken 2016; Van Hout et al. 2014). Due to the uncertain and intermittent nature of these resources, the predictability of electricity generation will be reduced (Holttinen et al. 2012; Doherty and O’Malley 2003; van Hout et al. 2014). Consequently, the electricity generation forecasts made in the wholesale electricity markets will deviate more strongly and more often from the actual real-time electricity generation, which sets higher requirements on the availability and activation of balancing reserves. Studies show that with higher penetration of intermittent renewable energy sources, the relative size of reserves increases as a percentage of the renewable capacity (Brouwer et al. 2014). In Europe, transmission system operators (TSOs) are responsible for different types of reserves to resolve these imbalances (European Commission 2017; TenneT 2016). Frequency containment reserves are provided by generators coupled synchronously to the grid and are activated automatically to stabilise the system frequency in case of a contingency event (Lampropoulos et al. 2012). Next, frequency restoration reserves with either automatic or manual activation restore the frequency and free the frequency containment reserves. In addition, in order to ensure the availability of sufficient balancing reserves in case of significant contingency events, the TSO contracts frequency restoration reserves in advance.
Positive balancing capacity, or upward adjustment, is activated when there is a shortage of electricity on the grid and can be realised by either increasing the electricity generation or reducing the electricity consumption. Negative balancing capacity, or downward adjustment, is needed when there is surplus of electricity on the grid and requires a reduction in electricity generation or an increase in electricity consumption.
The measures to adjust the electricity consumption are referred to as demand response (DR) options. DR is defined as “a change in the electricity consumption pattern of end-use consumers in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized” (U.S. Department of Energy 2006). DR is considered to be a cost-effective balancing option besides flexible electricity generation, electricity storage, and international transmission (van Hout et al. 2014; Doherty and O’Malley 2003; Cappers et al. 2010) and can be applied by different sectors ranging from large industrial electricity customers to smaller consumers such as households.
The increasing need for DR has enhanced the research, development, and promotion of DR in western European countries (IEA 2013, 2014; DENA 2010; Paulus and Borggrefe 2011; Klobasa 2009; Thema Consultancy Group 2014). The International Energy Agency (IEA) has developed an international program in which 16 countries work together to develop and promote DR which has stimulated research in the field (IEA 2014).
Theoretical balancing capacities of various energy-intensive industrial sectors as well as households have been evaluated for various EU countries. In a study commissioned by the transmission system operator (TSO) of the Netherlands, TenneT, a knowledge gap is identified with respect to the technical as well as economic DR potentials (van Hout et al. 2014). Furthermore, often the benefits of DR are assessed from a grid operator perspective, but if DR is to play a role in the balancing markets, it also needs to be evaluated from a balancing capacity supplier point of view (DENA 2010; Klobasa 2009; Paulus and Borggrefe 2011; Paulus and Borggrefe 2009).
To address this knowledge gap, this paper aims to assess the technical and economic DR potentials of a specific energy-intensive industrial process. In addition, this paper intends to provide more insights to what extent DR can influence the balancing markets and to increase the awareness of industries on their DR potential and the benefits they can gain in utilising this potential. Such an industrial process-specific assessment of the technical and economic DR potentials can support TSOs with the design of their DR programmes.
As a case study, this paper quantifies the positive and negative balancing capacity potential of the integrated steel plant of Tata Steel in IJmuiden (TSIJ) Netherlands. We focus on TSIJ because it is a single plant which is responsible for substantial share of total national electricity consumption (3% of the total Dutch electricity consumption). Moreover, industrial DR studies have shown that steel production processes are among the industrial processes with the highest DR potentials as stand-alone options (DENA 2010; Paulus and Borggrefe 2011; Paulus and Borggrefe 2009; Klobasa et al. 2009).
Besides being a large electricity consumer, TSIJ generates electricity from works arising gases (WAGs), which are gases that are produced in different stages of the steel manufacturing process. On an annual basis, TSIJ was even a net electricity exporter to the grid in 2013 with a consumption of ~ 2740 GWh and a generation of ~ 3500 GWh.
Automatic frequency restoration reserve
Manual frequency restoration reserve (non-contracted)
Manual frequency restoration reservea (contracted)
Activation ramp rate
≥7 % /min
≥100 % /PTU
≥100 % /PTU
TSIJ can offer DR to the power system by changing its net electricity demand profile for one or more certain programme time units1 (PTU), on request of TenneT (Frunt 2011). In the case TSIJ consumes less electricity from the grid by ramping up its power plant than announced day ahead for a particular PTU when the measure is called, this will be a positive demand response (PDR) measure. Similarly, ramping down TSIJ’s electricity production will lead to a net increase in TSIJ’s electricity consumption from the grid than declared day ahead for that particular PTU and will be considered a negative demand response (NDR) measure.
To assess the technical DR potentials, we develop a linear programming model in MATLAB which is solved by a simplex algorithm (Murty 1983). The objective function of the model is to maximise the PDR and NDR capacity TSIJ can provide at different PTUs in case there would be a call for balancing power. The objective function is subject to technical constraints. These limit the extent to which WAG flows to the two power plants can change and are determined among others by the WAG demand of all the TSIJ production plants, the ramp-up and ramp-down rate of the power plants, and the WAG storage capacity. By changing the allocation of the WAGs to the TSIJ power plants, i.e. the decision variables in the linear programming model, the PDR or NDR capacity is maximised.
Objective functions and decision variables
Positive demand response
The analysed PDR measures are based on ramping up the on-site electricity generation. This requires an increase in WAG allocation to power plants at the supply period. The supply period is the period at which there is a call for balancing capacity by the TSO.
- c k, t
is the average calorific value of WAG k (k can be COG, RBFG, or BOFG) for electricity generation at time interval t. These are 19, 5.2, and 8.0 MJ/Nm3, for COG, RBFG, and COFG, respectively.
- η u
is the efficiency of power plant u. We assume a constant efficiency factor for each power plant.2
- Fup u, k, t
is the additional WAG volumetric flow (Nm3/h) allocated to the power plant unit u at time t in case of a call for balancing capacity.
- Fnc u, k, t
is the WAG k allocation to power plant u within PTU t without a DR measure (under normal conditions (nc) (Nm3/h).
The Fncu, k, t amount is based on the day-ahead electricity generation planning. In case of a call for balancing capacity, the total gas k allocation to the power plant u at time unit t (Fu, k, t) should consist of the reference WAG allocation (Fncu, k, t) and the WAG allocation required for the additional electricity generation (Fupu, k, t) in order to provide balancing capacity (2). Thus, Fupu, k, t is the volumetric flow of WAG allocated to the power plant unit u at time t in addition to the reference amount of gas allocation (Fncu, k, t).
Negative demand response
- Fdown u, k, t
gives the reduction in the WAG k flow rate (Nm3/h) to power plant unit u as a DR measure at time interval t.
Fdownu, k, t is the decision variable for NDR. The maximum amount of NDR capacity (MaxNDRm) in MW is given by (4).
Mass balance constraints
Energy balance constraints
Fuel input constraints
Mass balance constraints
Energy balance constraints
The energy inflow and outflow from the system have to be balanced as energy cannot be produced or destroyed (8). Egenu, t gives the electricity generated (MWh) from power plant u at time interval t.
Operational constraints of power plants and gas holders
Reference electricity generation constraints
After the PDR and NDR measure, TenneT requires that the emergency capacity supplier goes back to its reference electricity generation/consumption level. If a supply period starts at time slot m and finishes at time slot M and T is the total number of time units in 1 day, the reference electricity generation constraint for the power plants is given by (14).
Energy demand constraint
The energy demand constraint assures that the PDR and NDR measures do not affect the WAGs and electricity available for on-site operations. Fsp, k, t is given as an input parameter in order to assure that the required WAGs are allocated to production processes. Therefore, the algorithm developed already makes sure that the electricity demand of the on-site processes is met. In case of PDR, the electricity generation is increased. This increase in generation is supplied as emergency capacity to the national grid; thus, the net amount of electricity and WAG available for on-site processes does not change.
Model implementation in MATLAB
The input data for the model includes WAG production rates by different processes, WAG consumption rates by different factories and boilers, and electricity generation in TSIJ power plants. For all these parameters, we obtained data with a time step of 15 min for 2014 from TSIJ’s database.
In order to quantify the PDR and NDR potentials, we conduct 100 model runs each. The beginning time slot, m, is input parameter in the model and is picked up manually for each model run. The time slots were chosen in such a way that they include daily and seasonal differences as well as periods with different production rates and WAG availabilities at Tata Steel in IJmuiden. The specific time slots for the model runs are chosen such that daily and seasonal variations are captured. Results from 100 model runs for PDR and 100 model runs for NDR have been analysed with respect to the size of the DR capacity, the availability rate3 of the DR capacity, and supply period as well as complexity of the measure. Moreover, in order to quantify the influence of future changes in factors such as power plant flexibility and WAG network constraints as well as emergency programme requirements, we conducted a sensitivity analysis.
Positive demand response potentials
For 20 MW PDR measures with a supply period of 2 PTUs, the binding constraint for the first PTU is the ramp rate of the power plant for 75% of the runs. In the second PTU of the measure, the binding constraints are the amount of RBFG available in the gas holder for the PDR measure and/or the ramp-down rate of the power plants depending to the WAG amount in the holders at that point in time. Ramping down is required in the PTU right after the balancing supply period ends in order to reach the reference electricity generation pattern and thus the RBFG flow rate in the reference situation.
Possible future conditions
In the previous section, it was shown that power plant ramp rate is among the main binding constraints for PDR measures with capacity higher than 10 MW and a supply period of 2 PTU. In order to calculate how future changes in power plant ramp rates can influence the PDR potentials, we conduct sensitivity analyses.
Negative demand response potentials
For 75% of the runs, we find that the maximum NDR supply period that can be achieved without affecting the electricity generation rates prior or after the measure is 1 h and 15 min (5 PTUs). The average NDR potentials for measures with supply periods between 2 PTUs and 5 PTUs are found to be above 20 MW/PTU in most of the cases.
One of the main binding constraints for NDR measures shorter or equal to 3 PTUs is the “reference electricity generation” constraint. This constraint requires that the electricity generation reaches its reference values (planned electricity generation levels) after the demand response measure. This constraint is linked to the ramp rate of power plants. Therefore, increasing the ramp rate of the power plant improves the NDR capacity of measures with supply period equal or shorter than 3 PTU. For measures longer than 3 PTUs, there is a higher gas storage demand than for shorter measures with the same capacity because the net amount of WAG power plant inflow reduction is higher. In this case, the binding constraint is the gasholder capacity.
TSIJ can offer 10 MW/PTU of PDR capacity with availability rate of 97% for a supply period of 30 min. This does not fulfil the capacity requirement of 20 MW/PTU for emergency capacity programmes in the Netherlands. That is why Tata Steel, with the current available PDR capacity, cannot participate in emergency capacity programs. The longer supply period comes at the cost of lower PDR capacity that is only half of the minimum bid size of 20 MW/PTU.
Towards 2030, the emergency capacity program requirements in the Netherlands are expected to change as the demand for emergency capacity enhances due to the increase in electricity generation from renewable energy sources (van Hout et al. 2014). If the availability rate required for participating in emergency demand response programs drops from 97 to 90%, TSIJ can commit to capacity of 15 MW/PTU, and if the availability rate requirement drops to 65%, TSIJ can commit for 20 MW/PTU with a supply period of 30 min. The latter would be sufficient for TSIJ to participate in emergency power programmes as a stand-alone emergency capacity provider. On the other hand, if emergency capacity programme requirements stay the same, TSIJ needs 50% increase in its power plant flexibility in order to have 20 MW/PTU PDR capacity.
Another option for Tata Steel to be able to use the PDR potentials is pooling with other PDR suppliers. Due to the increasing emergency capacity demand in the recent years, positive capacity pooling has gained importance in the Netherlands. Pooling programmes are likely to be successful in the future because they generally are able to provide higher capacities and availability rates than stand-alone options, decrease the administrative burden both for the TSO and for emergency program participants, and reduce the risk for suppliers.
With respect to the NDR potential, TSIJ can commit to 20 MW/PTU of NDR measure with a supply period of 3 PTUs and 80% availability under current WAG buffering capacities. If the RBFG gas holder capacities increase by 50%, it increases NDR capacity up to 25 MW/PTU for measures with a supply period of 4 PTUs. Negative emergency programmes have been only recently implemented in the Netherlands. Further analyses comparing the NDR capacity characteristics and capacity programme requirements are needed in order to assess the feasibility of participation for TSIJ.
Comparison of the results with other DR potential studies
The PDR capacities of TSIJ obtained by increasing the WAG input rate to power plants are found to be insufficient for participating in emergency demand response programmes in the Netherlands. Yet, the quantities and specifications of the PDR capacity can still be valuable. The total amount of PDR capacity of households in Germany by 2020 is estimated to be 53 MW/PTU (DENA 2010). In order to make this capacity available, high investment costs are required (DENA 2010). On the other hand, TSIJ can supply 10 MW/PTU for a supply period of 30 min with close to zero investment requirements. This is a substantial value especially when compared to the total capacity of the German household sector.
Theoretical demand PDR potentials of industrial sectors in Germany, such as cement, aluminium (electrolysis), chlorine (based on chlorine alkali electrolysis membrane method), and paper production are found to be 314, 277, 685, and 311 MW respectively (DENA 2010). The average NDR of Germany energy-intensive industrial sectors such as chlorine and paper industry are 346 and 94 MW NDR potential respectively (DENA 2010; Paulus and Borggrefe 2009). For TSIJ, we obtained 20 MW/PTU of NDR capacity for a supply period of 45 min with an availability rate ≥97%. The PDR and NDR potentials that we obtained for TSIJ are substantially lower than these values. The first reason for this is that these are sectoral estimates while we concentrated specifically to the Tata Steel plant in IJmuiden. In addition, we looked into the technical potentials that are generally lower than the theoretical potentials and we evaluated the potentials of one DR measure and not the entire plant (e.g. “production process shutdowns” were excluded).
Assessment of the method
DR potentials through production planning for incentive-based demand response programs (e.g. tertiary balancing capacity) (DENA 2010; Paulus and Borggrefe 2011; Klobasa 2009; Gils 2014; Klobasa et al. 2009)
DR potentials through electricity demand planning based on electricity price (e.g. time of use programmes) (Ashok and Banerjee 2000; Ashok 2006; Bego et al. 2014; Fernandez et al. 2013; Hadera et al. 2014; Lee and Reklaitis 1995; Mignont and Hermia 1996; Wang and Li 2013)
These studies, in which the balancing capacity supplied by shutting down steel production processes are assessed, mainly focus on the theoretical DR potential. The first group calculates the DR potentials based on the electricity intensities of the processes while it is not taking into account how much of these potentials can actually be realised based on the technical constraints of the processes involved in the demand response measure. On the other hand, the linear optimisation MATLAB model used in our study takes into account the technical constraints of the WAG network and the constraints of each unit involved in the measure. This allows for a more accurate estimation of the potentials as it gives the technical potential of what actually can be achieved instead of the theoretical potential. The second group of studies, on the other hand, mainly concentrates on production planning based on electricity prices. The models developed as part of these sets of studies do not give insight on how those processes can be used for balancing capacity purposes such as emergency balancing capacities considered in this study.
There are also various limitations of this study. Firstly, it was assumed that the change in WAG flow rate to the power plants will not affect the parts of the WAG network connecting to the on-site processes. However, this cannot always be assured because an increase in the pressure in one part of the network can lead to a change in the other parts. Secondly, in the model, we only include technical constraints of the WAG and power plants at TSIJ. However, in addition to technical constraints, there are also contract-related constraints. For example, the amount of WAGs that TSIJ can send to the power plants is limited by contract agreements between the two parties. Thirdly, the PDR and NDR capacities in this study are statistical representations for model runs of 100 days. In order to increase the accuracy of the results, the number of runs should be increased.
In this research, we have used linear optimisation programming in order to evaluate the positive demand response and negative demand response potentials of Tata Steel in IJmuiden. We need these estimates in order to analyse the feasibility of TSIJ to participate in electricity emergency capacity programs.
The study concludes that TSIJ cannot fulfil the requirements for participating in demand response programs in the Netherlands that require 20 MW/PTU with availability rate of 97% for at least 2 PTUs. For this availability level, TSIJ can provide up to 10 MW/PTU for a supply period of 2 PTUs. Therefore, there are two options for Tata Steel to participate on DR programs in the future. The first option is to pool with other DR suppliers. Pooling reduces the capacity and availability requirements giving TSIJ a chance to utilise its capacity while reducing the risk of failing to provide the DR capacity when required. This option is viable but requires more in-depth research on DR pooling opportunities available for TSIJ. The second option is increasing the power plant flexibility of TSIJ. An increase in power plant ramp rate by 50% is required in order for TSIJ to be able to provide 20 MW/PTU PDR. However, this is not a feasible option due to high costs involved.
TSIJ can provide 20 MW/PTU of NDR measures with a supply period of 3 PTUs and 80% availability with WAG storage capacity being the main binding constraint. 20 MW/PT of NDR capacity for a supply period of 4 PTUs and availability rate of 95% can be reached through 35% increase in the RBFG storage capacity. The feasibility of participating in NDR emergency capacity programmes has to be evaluated in the future as NDR emergency programmes develop in the Netherlands.
The results of this research contribute in developing a knowledge base for demand response potentials in the Netherlands and improve TSIJ’s demand response strategy. In addition, these research results provide the TSO insight into the demand response potentials of big electricity consumers such as TSIJ and the suitability of these potentials to the emergency capacity programmes currently in place.
Recommendations for future research
Regarding the future positioning of TSIJ in the balancing markets, it is important to analyse the emergency capacity pooling options in the Netherlands. This involves, but is not limited to, a detailed analysis of the current and future conditions for participating in emergency pools and benefits obtained from such participation. Moreover, the modelling approach developed in this study can be used for evaluating the PDR and NDR capacities of other steel plants as well as other industrial processes.
PTU is the programme time unit of the intra-day balancing market. The length of the PTU on the balancing market is country dependent. Generally, it is 15, 30, or 60 min. In the Netherlands, the length of the PTU is 15 min.
The efficiency of the power plant depends on various factors such as gas quality and type of the gas used as well as the amount of gas. In order to limit the model complexity, we assume a constant power plant efficiency rate.
The availability rate gives the percentage of the emergency capacity calls that TSIJ can provide the required DR capacity.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Brouwer, A. S., van den Broek, M., Seebregts, A., & Faaij, A. (2014). Impacts of large-scale intermittent renewable energy sources on electricity systems, and how these can be modeled. Renewable and Sustainable Energy Reviews, 33, 443–466. https://doi.org/10.1016/j.rser.2014.01.076.CrossRefGoogle Scholar
- DENA. (2010). Integration of renewable energy sources in the German power supply system from 2015–2020, Berlin.Google Scholar
- Doherty, R. & O’Malley, M. (2003). Quantifying reserve demands due to increasing wind power penetration. In IEEE Bologna PowerTech - Conference Proceedings. pp. 59–63.Google Scholar
- European Commission. (2016). Proposal for a Directive of the European Parliament and of the Council on the promotion of the use of energy from renewable sources (recast) COM/2016/0767 final/2–2016/0382 COD) http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52016PC0767R%2801%29.
- European Commission. (2017). Commission Regulation (EU) 2017/1485 of 2 August 2017 establishing a guideline on electricity transmission system operation (Text with EEA relevance.). http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32017R1485.
- Frunt, J. (2011). Analysis of balancing requirements in future sustainable and reliable power systems. Technical University of Eindhoven. Available at: http://alexandria.tue.nl/extra2/711271.pdf.
- Holttinen, H., Milligan, M., Ela, E., Menemenlis, N., Dobschinski, J., Rawn, B., Bessa, R. J., Flynn, D., Gomez-Lazaro, E., & Detlefsen, N. K. (2012). Methodologies to determine operating reserves due to increased wind power. IEEE Transactions on Sustainable Energy, 3(4), 713–723. https://doi.org/10.1109/TSTE.2012.2208207.CrossRefGoogle Scholar
- van Hout, M., Koutstaal, P., Ozdemir, O., & Seebregts, A. (2014). Quantifying flexibility markets. Petten: ECN Available at: http://www.tennet.eu/nl/fileadmin/downloads/About_Tennet/Publications/Technical_Publications/TenneT_ECN_Quantifying_flexibility_markets.pdf.Google Scholar
- International Energy Agency. (2013). Implementing agreement on demand-side management technologies and programmes: 2012 annual report, Stockholm. Available at: http://www.ieadsm.org/Files/ExcoFileLibrary/Annual Reports/2012_Annual_Report.pdf.
- International Energy Agency. (2014). IEA DSM. Available at: http://www.ieadsm.org/Publications.aspx?ID=18. Accessed 5 Nov 2014.
- JRC. (2017). NREAPs and progress reports DATA PORTAL. Joint Research Center, European Commission. Last updated 24 October 2017. http://iet.jrc.ec.europa.eu/remea/nreaps-and-progress-reports-data-portal.
- Klobasa, M. (2009). Dynamische Simulation eines Lastmanagements und Integration von Windenergie in ein Elektrizitätsnetz Dynamische Simulation eines Lastmanagements und Integration von Windenergie in ein Elektrizitätsnetz, Fraunhofer IRB.Google Scholar
- Klobasa, M., Erge, T., & Wille-haussmann, B. (2009). Integration von Windenergie in ein zukünftiges Energie- system unterstützt durch Lastmanagement, Freiburg.Google Scholar
- Lampropoulos, I., et al. (2012). Analysis of the market-based service provision for operating reserves in the Netherlands. In European Energy Market (EEM), 9th International Conference on the IEEE. pp. 1–8.Google Scholar
- Ministerie van Economische Zaken. (2016). Energierapport Transitie naar Duurzaam. Dutch Ministry of Economic Affairs. The Hague: Netherlands.Google Scholar
- Murty, K. (1983). Linear programming. Wiley, http://eu.wiley.com/WileyCDA/WileyTitle/productCd-047109725X.html.
- Paulus, M., & Borggrefe, F. (2009). Economic potential of demand side management in an industrialized country—the case of Germany. In 10th IAEE European conference, energy, policies and technologies for sustainable economies. Vienna: 10th IAEE European Conference, Energy, Policies and Technologies for sustainable economies, pp. 7–10.Google Scholar
- Rijksoverheid. (2016). Rijksoverheid stimuleert duurzame energie. https://www.rijksoverheid.nl/onderwerpen/duurzame-energie/meer-duurzame-energie-in-de-toekomst.
- TenneT. (2016). Market review 2016. Electricity market insights. TenneT, Arnhem, Netherlands. https://www.tennet.eu/fileadmin/user_upload/Company/Publications/Technical_Publications/Dutch/2016_Market_Review_TenneT.pdf.
- Thema Consultancy Group. (2014). Demand response in the Nordic electricity market: input to strategy on demand flexibility. Copenhagen: Norden.Google Scholar
- U.S. Department of Energy. (2006). Benefits of demand response in electricity markets and recommendations for achieving them, Available at: http://energy.gov/sites/prod/files/oeprod/DocumentsandMedia/DOE_Benefits_of_Demand_Response_in_Electricity_Markets_and_Recommendations_for_Achieving_Them_Report_to_Congress.pdf.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.