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Figure 1: Categorization of Demand Side Management (based upon [26])

- «Green tariffs» (1956) for large firms or buildings (La Defense): Many prices options according to season/hour and localisation/use.
- «Off-peak hours» (1965) tariffs for residential market and business– special tariffs from 10 PM to 6AM week days and on Sundays.
- «Peak day step back» (EJP) (1982) for residential market was introduced to decrease consumption at critical times (22 days of 18 hours between 1st November and 31th March). It established high price during this period and low price the rest of the year. Currently, EJP replaced by TEMPO (6 price’s levels according to hour and season).

*2: Different time horizon in DSM, based upon [1]*

The above mentioned general description of the portfolio balancing problem for city districts and neighborhoods incorporate several challenges both for the mathematical method and the overall approach. First, there is usually a high heterogeneity of participants and devices that must be taken into account. Residential buildings, but also industrial consumers might take part of the portfolio balancing. The load and flexibility of such units diversify within their granularity of time, their amplitude and their criticalness. Second, a city district contains in general a high number of participants and devices which lead to a computational intensive problem with an increasing portfolio size. Consequently, a mathematical optimization must be able to handle a large amount of heterogeneous participants. Third, referring to the concept of demand response and in particular to direct load control, it is an important requirement for the method to ensure data privacy. Fourth, the coordination within city districts usually needs to integrate both local (customer) and global (system) level objectives. In respect to this challenge the method requires an approach for both satisfying global and local objectives. Fifth, depending on the kind of installed devices on the demand side the mathematical optimization method might have to be able to take care of on/off devices leading to an Integer related problem formulation.**Research Paper and Solver**

Indirect load control on the demand side is for example studied in [5] and [6]. In particular [6] is a very recent example for showing the operation scheduling of Plug-in electric vehicles coordinated by an aggregator agent. The MILP is solved within GAMS Build 21.1.2. using the CPLEX 12.5.1 solver [7].

This research satisfies all of the mentioned requirements. As mentioned in the challenges above a central optimization becomes hard to solve with an increasing portfolio size. Indirect and direct load control for scheduling loads on the demand side by using a distributed algorithm is hence an active field of research. Consequently many research papers, such as [3, 5, 8–13] propose distributed optimization demand response techniques for (residential) energy demand side management. Decomposition methods such as in [13] or [14] use dual decomposition (DD) or the alternating direction method of multipliers (ADMM) such as in [3, 12]. For both DD and ADMM in particular challenges and requirements 1) – 4) are taken into account. [10]

The residential demand side energy management in [14] for example used the matlab environment in combination with ILOG CPLEX 12.2 to solve the optimization problems. The ADMM problems were solved using CVX, a package for specifying and solving convex programs [15], [16]. Looking into integer related problem formulations authors in [17] propose a column generation approach for direct load control which is solved using the object-oriented Python Interface of Gurobi [18]. Research in [10] performs a decentralized robust ILP optimization for balancing a portfolio within a microgrid. The optimization uses the CPLEX package within Java. Both [17] and [10] are other examples for satisfying all mentioned challenges 1) - 5) and the resulting requirements. [19] uses a MILP formulation for the optimal control of a residential microgrid using the Gurobi solver as well through the object-oriented interface for Java. Further, authors in [20] perform a distributed optimization via a multi-agent system using the Java agent development framework (JADE) [21]. However, each local agent solves its own local MILP optimization using MOSEK [22].

References

[1] P. Palensky and D. Dietrich, “Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads,” Industrial Informatics, IEEE Transactions on, vol. 7, no. 3, pp. 381–388, 2011.

[2] Smart Grid Task Force, “Regulatory Recommendations for the Deployment of Flexibility: SGTF-EG3 Report,” https://ec.europa.eu/energy/sites/ener/files/documents/EG3%20Final%20-%20January%202015.pdf, 2015.

[3] Morten Juelsgaard, “Utilizing Distributed Resources in Smart Grids A Coordination Approachin Smart Grids A Coordination Approach: A Coordination Approach,” Dissertation, Aalborg University, Denmark, 2014.

[4] OpenADR Alliance. Available: http://www.openadr.org/ (2016, Feb. 19).

[5] A. Safdarian, M. Fotuhi-Firuzabad, and M. Lehtonen, “A Distributed Algorithm for Managing Residential Demand Response in Smart Grids,” IEEE Trans. Ind. Inf, p. 1, 2014.

[6] I. Momber, S. Wogrin, and Gomez San Roman, T, “Retail Pricing: A Bilevel Program for PEV Aggregator Decisions Using Indirect Load Control,” Power Systems, IEEE Transactions on, vol. 31, no. 1, pp. 464–473, 2016.

[7] IBM Corporation, IBM CPLEX Optimizer - United States. Available: http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/ (2016, Feb. 18).

[8] N. Rahbari-Asr and M.-Y. Chow, “Cooperative Distributed Demand Management for Community Charging of PHEV/PEVs Based on KKT Conditions and Consensus Networks,” IEEE Trans. Ind. Inf, vol. 10, no. 3, pp. 1907–1916, 2014.

[9] del Real, Alejandro J, A. Arce, and C. Bordons, “An Integrated Framework for Distributed Model Predictive Control of Large-Scale Power Networks,” IEEE Trans. Ind. Inf, vol. 10, no. 1, pp. 197–209, 2014.

[10] E. Kuznetsova, C. Ruiz, Y.-F. Li, and E. Zio, “Analysis of robust optimization for decentralized microgrid energy management under uncertainty,” International Journal of Electrical Power & Energy Systems, vol. 64, pp. 815–832, 2015.

[11] Elizaveta Kuznetsova, “Microgrid Agent-Based Modelling And Optimization Under Uncertainty,” Dissertation, Universite de Versailles, 2014.

[12] M. Kraning, E. Chu, J. Lavaei, and S. P. Boyd, Dynamic network energy management via proximal message passing.

[13] Y. J. Jhi and M. D. Ilic, “Multi-Layered Optimization Of Demand Resources Using Lagrange Dual Decomposition,” Smart Grid, IEEE Transactions on, vol. 4, no. 4, pp. 2081–2088, 2013.

[14] B. Moradzadeh and K. Tomsovic, “Two-Stage Residential Energy Management Considering Network Operational Constraints,” IEEE Trans. Smart Grid, vol. 4, no. 4, pp. 2339–2346, 2013.

[15] Michael Grant and Stephen Boyd, CVX: Matlab software for disciplined convex programming, version 2.0 beta. http://cvxr.com/cvx.

[16] Michael Grant and Stephen Boyd. Graph implementations for nonsmooth convex programs, Recent Advances in Learning and Control (a tribute to M. Vidyasagar), V. Blondel, S. Boyd, and H. Kimura, editors, pages 95-110, Lecture Notes in Control and Information Sciences, Springer, 2008. http://stanford.edu/~boyd/graph_dcp.html.

[17] H. Harb, J.-N. Paprott, P. Matthes, T. Schütz, R. Streblow, and D. Müller, “Decentralized scheduling strategy of heating systems for balancing the residual load,” Building and Environment, vol. 86, no. 0, pp. 132–140, http://www.sciencedirect.com/science/article/pii/S0360132314004260, 2015.

[18] Gurobi, Gurobi Optimization, Inc. Available: http://www.gurobi.com/ (2015, Jun. 15).

[19] P. O. Kriett and M. Salani, “Optimal control of a residential microgrid,” Energy, vol. 42, no. 1, pp. 321–330, 2012.

[20] N. Blaauwbroek, P. H. Nguyen, M. J. Konsman, Huaizhou Shi, Kamphuis, R. I. G, and W. L. Kling, “Decentralized Resource Allocation and Load Scheduling for Multicommodity Smart Energy Systems,” Sustainable Energy, IEEE Transactions on, vol. 6, no. 4, pp. 1506–1514, 2015.

[21] Telecom Italia SpA, Jade Site | Java Agent DEvelopment Framework Available: http://jade.tilab.com. Available: http://jade.tilab.com/ (2016, Feb. 18).

[22] MOSEK ApS MOSEK Optimization Toolbox [Online]. Available: http://mosek.com/. Available: https://www.mosek.com/ (2016, Feb. 18).

##### Contributors:

Mr [23] Wei-Yu Chiu; Hongjian Sun; H.V. Poor (2012). “Demand-side energy storage system management in smart grid”. 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm): no.73, 78, p.5–8, 2012.

[24] Lund, Peter D; Lindgren, Juuso; Mikkola, Jani; Salpakari, Jyri (2015). “Review of energy system flexibility measures to enable high levels of variable renewable electricity”. Renewable and Sustainable Energy Reviews. no. 45, p.785–807, 2015.

[25] A. A. Garcia, “Demand Side Integration,” Working Group C6.09, Cigre Technical Report, August 2011

[26] Lampropoulos I, Kling W L, Ribeiro P F and van den Berg J (2013). “History of demand side management and classification of demand response control schemes”. IEEE Power & Energy Society General Meeting (Vancouver BC), 2013.

[27] OECD, “OECD Economic Surveys: France 2011”, March 11, 2011.

[28] EDF Group: Demand Response and Pricing in France: EDF’s experience, New regulation, Main goals. 2007, April. Available http://smartenergydemand.eu/wp-content/uploads/2011/05/EDF-07-04-10-Dynamic-Pricing-in-France.ppt (2017, May 14). **Contributor**:

Dr. Elizaveta Lebedeva, University of Tartu

Dr. Michael Diekerhof, RWTH Aachen UniversityUniversit

Prof Antonello Monti, RWTH Aachen University