As for example stated in [1], the energy system optimization on the generation and distribution side is well recognized and part of many research articles. However, due to an increasing amount of distributed generation and flexibility at the consumer side, the demand side optimization becomes an important field of research within the energy management. Unidirectional and top-down is the traditional way of operation of electrical energy system. For balancing supply and demand, the generation of large power plants is coordinated. That is typically a classic unit commitment problem. However, the achievement of balancing supply and demand is challenged by the increasing number of volatile renewable generation, i.e. wind and solar energy, but also by the increasing amount of electric vehicles (EV) and electro-thermal heating systems, such as heat pumps (HP) or small combined heat and power (CHP) units. [1]

We define DR as part of DSM similar to [1] and [2], as the “voluntary changes by end-consumers or producers or at storages of their usual electricity/gas flow patterns - in response to market signals such as time-variable prices, incentive payments” or beforehand given agreements between customers and third parties. Such pattern changes are possible due to flexibility on the demand side. Such flexibility might be provided for example through electrical or thermal storages where demand is decoupled from generation, but also from other flexible loads, such as EVs.

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

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].

[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 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).

Mr Michael Diekerhof, RWTH Aachen University

Prof Antonello Monti, RWTH Aachen University