Skip to main content

An economic dispatch algorithm for congestion management of smart power networks

An oblivious routing approach

Abstract

We present a novel oblivious routing economic dispatch (ORED) algorithm for power systems. The method is inspired by the oblivious network design which works perfectly for networks in which different sources (generators) send power flow toward their destinations (load points) while they are unaware of the current network state and other flows. Basically, our focus is on the economic dispatch while managing congestion and mitigating power losses. Furthermore, we studied a loss-minimizing type of the economic dispatch which aims to minimize the emission by optimizing the total power generation rather than system cost. Comparing to state-of-the-art economic dispatch methods, our algorithm is independent of network topology and works for both radial and non-radial networks. Our algorithm is thus suited for large-scale economic dispatch problems that will emerge in the future smart distribution grids with host of small, decentralized, and flexibly controllable prosumers; i.e., entities able to consume and produce electricity. The effectiveness of the proposed ORED is evaluated via the IEEE 57-bus standard test system. The simulation results verify the superior performance of the proposed method over the current methods in the literature in terms of congestion management and power loss minimization.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Notes

  1. Asymptotic analysis is a method of describing limiting behaviors of functions. Assuming that f(n) and g(n) are two functions defined on some subset of the real numbers, \(f(n)=O(g(n))\) if there exists constants c and N such that \(f(n)\le cg(n)\) for all \(n>N\). In this definition, g(n) is called an asymptotic high-threshold (upper-bound) for f(n), or f(n) is called to be asymptotically bounded to g(n).

  2. As this paper focuses on the novel method for solving the economic dispatch problem, we assume the throughput of the generator to be deterministic. This way, the method can be illustrated more clearly.

  3. Diameter of a graph is defined as the maximum distance between any pair of nodes in the graph.

  4. Partition of set A is a set of A subsets \(\{A_i|i\in [1,k]\}\) such that \(\bigcup \nolimits _{i=1}^k A_i = A\), \(\forall i\in [1,k]:A_i\ne \emptyset \), and \(\forall i\ne j: A_i\cap A_j = \emptyset \).

  5. The distance \(d_{\mathcal {G}}(u,v)\) between nodes uv is defined as the length of shortest path between u and v in \(\mathcal {G}\). The length of a path in a weighted graph is defined as the total weight of the edges participating in the path.

  6. Set \(B_{\mathcal {G}}(v,r)\in V\) is called a ball of center \(v\in V\) of radius r if \(u\in B_{\mathcal {G}}(v,r)\) iff \(d_{\mathcal {G}}(u,v)\le r\).

  7. Permutation \(\pi \) on finite set A of size n is a one-to-one and onto function mapping set \(\{1,2,\ldots ,n\}\) to set A.

  8. In this paper, path of a graph is considered as a simple path and represented by a subset of edge set E such that there exist a permutation of edges in a path where the first edge is incident to the start node of the path, each two consecutive edges are incident to a common node, and the last edge is incident to the end node of the path.

References

  1. Kar, S., Hug, G., Mohammadi, J., Moura, J.M.F.: Distributed state estimation and energy management in smart grids: a consensus+ innovations approach. IEEE Trans. Select. Topics Signal Process. 8(6), 1022–1038 (2014)

    Article  Google Scholar 

  2. U.S. Department of Energy: The Smart Grid: An Introduction. (2008)

  3. Zidan, A., El-Saadany, E.F.: A cooperative multi-agent framework for self-healing mechanisms in distribution systems. IEEE Trans. Smart Grid 3(3), 1525–1539 (2012)

    Article  Google Scholar 

  4. Brown, R.E.: Impact of smart grid on distribution system design. In: Proceedings of IEEE Power and Energy Society General Meeting, pp. 1–4, Pittsburgh, PA (2008)

  5. Chowdhury, B.H., Rahman, S.: A review of recent advances in economic dispatch. IEEE Trans. Power Syst. 5(4), 1248–1259 (1990)

    MathSciNet  Article  Google Scholar 

  6. Pappu, V., Carvalho, M., Pardalos, P.: Optimization and Security Challenges in Smart Power Grids. Springer, New York (2013)

    Book  Google Scholar 

  7. Zhu, J.: Optimization of Power System Operation. Wiley, Amsterdam (2014)

    Google Scholar 

  8. Padhy, N.: Unit commitment: a bibliographical survey. IEEE Trans. Power Syst. 19(2), 1196–1205 (2004)

    Article  Google Scholar 

  9. Selvakumar, A.I., Thanushkodi, K.: A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans. Power Syst. 22(1), 42–51 (2007)

    Article  Google Scholar 

  10. Amini, M.H., Nabi, B., Haghifam, M.-R.: Load management using multi-agent systems in smart distribution network. In: Proceedings of IEEE Power and Energy Society General Meeting, Vancouver, BC, Canada, pp. 1–5 (2013)

  11. Lavaei, J., Tse, D., Zhang, B.: Geometry of power flows and optimization in distribution networks. IEEE Trans. Power Syst. 29(2), 572–583 (2014)

    Article  Google Scholar 

  12. Sanjari, M.J., Karami, H., Gooi, H.B.: Micro-generation dispatch in a smart residential multi-carrier energy system considering demand forecast error. Energy Convers. Manag. 120, 90–99 (2016)

    Article  Google Scholar 

  13. Kellerer, E., Steinke, F.: Scalable economic dispatch for smart distribution networks. IEEE Trans. Power Syst. 99, 1–8 (2014)

    Google Scholar 

  14. Carrion, M., Arroyo, J.: A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Trans. Power Syst. 21(3), 1371–1378 (2006)

    Article  Google Scholar 

  15. Botterud, A., Zhi, Z., Wang, J., et al.: Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: a case study of Illinois. IEEE Trans. Sustain. Energy 4(1), 250–261 (2012)

    Article  Google Scholar 

  16. Li, Z., Guo, Q., Sun, H., Wang, J.: Sufficient conditions for exact relaxation of complementarity constraints for storage-concerned economic dispatch. IEEE Trans. Power Syst. 99, 1–2 (2015)

    Google Scholar 

  17. Khator, S.K., Leung, L.C.: Power distribution planning: a review of models and issues. IEEE Trans. Power Syst. 12(3), 1151–1159 (1997)

    Article  Google Scholar 

  18. Liu, Y., Ul Hassan, N., Huang, S., Yuen, C.: Electricity cost minimization for a residential smart grid with distributed generation and bidirectional power transactions. IEEE Innovat. Smart Grid Technol. (ISGT), pp. 1–6, Washington, DC (2013)

  19. Papavasiliou, A., Oren, S.S.: Supplying renewable energy to deferrable loads: algorithms and economic analysis. In: Proceedings of IEEE Power and Energy Society General Meeting, pp. 1–8, Minneapolis, MN (2010)

  20. Boroojeni, K.G., et al.: Optimal two-tier forecasting power generation model in smart grids. Int. J. Inf. Process. 8(4), 79–88 (2014)

    Google Scholar 

  21. Iyengar, S.S., Boroojeni, K.G.: Oblivious Network Routing: Algorithms and Applications. MIT Press, New York (2015)

    MATH  Google Scholar 

  22. Gupta, A., Hajiaghayi, M.T., Racke, H.: Oblivious network design. In: SODA 06: Proceedings of the 17th Annual ACM-SIAM Symposium on Discrete Algorithm. New York, NY, USA: ACM, pp. 970–979 (2006)

  23. Maknouninejad, A., Lin, W., Harno, H.G., Qu, Z., Simaan, M.A.: Cooperative control for self-organizing microgrids and game strategies for optimal dispatch of distributed renewable generations. Energy Syst. 3(1), 23–60 (2012)

    Article  Google Scholar 

  24. Tuffaha, M., Gravdahl, J.T.: Discrete state-space model to solve the unit commitment and economic dispatch problems. Energy Syst. pp. 1–23, in Press (2016)

  25. Frank, S., Rebennack, S.: An introduction to optimal power flow: theory, formulation, and examples. IIE Trans. to appear (2016)

  26. Nolden, C., Schönfelder, M., Eßer-Frey, A., Bertsch, V., Fichtner, W.: Network constraints in techno-economic energy system models: towards more accurate modeling of power flows in long-term energy system models. Energy Syst. 4(3), 267–287 (2013)

    Article  Google Scholar 

  27. Kargarian, A., Mohammadi, J., Guo, J., Chakrabarti, S., Barati, M., Hug, G., Kar, S., Baldick, R.: Toward distributed/decentralized DC optimal power flow implementation in future electric power systems. IEEE Trans Smart Grid (2016, to appear)

  28. Rebennack, S., Flach, B., Pereira, M.V.F., Pardalos, P.M.: Stochastic hydro-thermal scheduling under CO2 emissions constraints. IEEE Trans. Power Syst. 27(1), 58–68 (2012)

    Article  Google Scholar 

  29. Shortle, J., et al.: Transmission-capacity expansion for minimizing blackout probabilities. IEEE Trans. Power Syst. 29(1), 43–52 (2014)

    Article  Google Scholar 

  30. Tucker, A.: A note on convergence of the Ford–Fulkerson flow algorithm. Math. Oper. Res. 2(2), 143–144 (1977)

    MathSciNet  Article  MATH  Google Scholar 

  31. Kumar, A., Gupta, A., Roughgarden, T.: Simpler and better approximation algorithms for network design. In: Proceedings of the 35th STOC, pp. 365–372 (2003)

  32. Bartal, Y.: On approximating arbitrary metrics by tree metrics. In: Proceedings of the 30th STOC, pp. 161–168 (1998)

  33. Alman, F.S.S., Heriyan, J.C., Avi, R.R., Ubramanian, S.S.: Approximating the single-sink link-installation problem in network design. SIAM J. Optim. 11, 595–610 (2000)

    MathSciNet  Article  Google Scholar 

  34. Arger, D.R.K., Inkoff, M.M.: Building Steiner trees with incomplete global knowledge. In: Proceedings of the 41st FOCS, pp. 613–623 (2000)

  35. Uha, S.G., Eyerson, A.M., Ungala, K.M.: Hierarchical placement and network design problems. In: Proceeding of the 41st FOCS, pp. 603–612 (2000)

  36. Uha, S.G., Eyerson, A.M., Ungala, K.M.: A constant factor approximation for the single sink edge installation problem. In: Proceedings of the 33rd STOC, pp. 383–388 (2001)

  37. Eyerson, A.M., Ungala, K.M., Lotkin, S.A.P.: Cost-distance: two metric network design. In: Proceedings of the 41st FOCS, pp. 624–630 (2000)

  38. Rebennack, S., Nahapetyan, A., Pardalos, P.M.: Bilinear modeling solution approach for fixed charge network flow problems. Optim. Lett. 3(3), 347–355 (2009)

    MathSciNet  Article  MATH  Google Scholar 

  39. Srinivasagopalan, S., Busch, C., Iyengar, S.S.: An oblivious spanning tree for single-sink buy-at-bulk in low doubling-dimension graphs. IEEE Trans. Comput. 61(5), 700–712 (2012)

    MathSciNet  Article  Google Scholar 

  40. Kumar, A., Gupta, A., Roughgarden, T.: Approximations via cost-sharing: a simple approximation algorithm for the multicommodity rent-or-buy problem. In: Proceedings of the 44th FOCS, pp. 606–615 (2003)

  41. Garg, N., Khandekar, R., Konjevod, G., Ravi, R., Salman, F.S., Sinha, A.: On the integrality gap of a natural formulation of the single-sink buy-at-bulk network design formulation. In: Proceedings of the 8th IPCO, pp. 170–184 (2001)

  42. Charikar, M., Karagiozova, A.: On non-uniform multicommodity buy-at-bulk network design. In: Proceedings of the 37th STOC, pp. 176–182 (2005)

  43. Alwar, K.T.: Single-sink buy-at-bulk LP has constant integrality gap. In: Proceedings of the 9th IPCO, pp. 475–486 (2002)

  44. Kumar, A., Gupta, A., Roughgarden, T.: A constant-factor approximation algorithm for the multicommodity rent-or-buy problem. In: Proceedings of the 43rd FOCS, pp. 333–342 (2002)

  45. Werbuch, B.A., Zar, Y.A.: Buy-at-bulk network design. In: Proceedings of the 38th FOCS, pp. 542–547 (1997)

  46. Frank, S., Steponavice, I., Rebennack, S.: Optimal power flow: a bibliographic survey I: formulations and deterministic methods. Energy Syst. 3(3), 221–258 (2012)

    Article  Google Scholar 

  47. Frank, S., Steponavice, I., Rebennack, S.: Optimal power flow: a bibliographic survey II: non-deterministic and hybrid methods. Energy Syst. 3(3), 259–289 (2012)

    Article  Google Scholar 

  48. Amini, M.H., et al.: Distributed security constrained economic dispatch. IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA) (2015)

  49. Wood, A.J., Wollenberg, B.F.: Power Generation, Operation, and Control. Wiley, Amsterdam (2012)

    Google Scholar 

  50. Fakcharoenphol, J., Rao, S.B., Talwar, K.: A tight bound on approximating arbitrary metrics by tree metrics. In: Proceedings of the 35th STOC, pp. 448–455 (2003)

  51. MATLAB version 8.5. Miami, Florida: The MathWorks Inc. (2015)

  52. Abdollahi, A., Moghaddam, M.P., Rashidinejad, M., Sheikh-El-Eslami, M.K.: Investigation of economic and environmental-driven demand response measures incorporating UC. IEEE Trans. Smart Grids 3(1), 12–25 (2012)

    Article  Google Scholar 

  53. Power systems test case archive, 57 Bus Power Flow Test Case. [Online]. Available: http://www2.ee.washington.edu/research/pstca/pf57/pg_tca57bus.htm (1993). Accessed 10 May 2016

  54. Zimmerman, R.D., Murillo-Sanchez, C.E., Thomas, R.J.: MATPOWER: Steady-state operations, planning and analysis tools for power systems research and education. IEEE Trans. Power Syst. 26(1), 12–19 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Hadi Amini.

Appendix

Appendix

In contrast to the traditional MCFPs that are defined with a well-defined set of commodities (with specific size and source/destination nodes) and given flow cost function for every edge, oblivious network routing aims to solve an MCFP in which either the flow cost function is not specified (flow cost is oblivious), or the commodities size, source, and destination are not specified (commodities are oblivious).

Oblivious network routing approach solves oblivious MCFPs by employing a practical method not guaranteed to be perfect, but sufficient for the immediate goal which is obtaining an acceptable approximation of the optimal solution. In this regard, oblivious network routing is considered to be a heuristic method since it can be used to speed up the process of finding a satisfactory solution (while computing the optimal solution is impractical).

Oblivious network routing is different with Dynamic (adaptive) Routing (DR) since DR proposes a different solution in response to any change of the MCFP oblivious components; while, oblivious routing deploys a single routing scheme for an oblivious MCFP with the aim of approximating the optimal solution of the problem with oblivious parameters.

The common characteristics of the oblivious routing schemes includes being pretty flexible to the obliviousness of the environment in which the commodities are flowing and making the traffic flows distributed over the network and preventing the flow-congestion in some specific nodes or edges. Additionally, these types of routing schemes provide a low-cost flow routing in long term even if a wide range of unpredictable events occur in the network like bursty flow derived from a specific node or failure of some node in forwarding the flow through the network. In fact, the versatile routing schemes best fit to those networks that we have little/no knowledge regarding their current and future states.

Preliminaries to oblivious network routing

At first, we provide some basic definitions and preliminary data structures needed to explain the \(\mathcal {ORED}\) algorithm. In the rest of the paper, \(\mathcal {G}=(V,E,w)\) is considered as a weighted connected graph of diameterFootnote 3 \(2^h\) (for some integer h) where \(w_e>1\) for every \(e\in E\). The mentioned conditions on the graph diameter and its weight function don’t reduce its generality because any weighted connected graph can be converted to \(\mathcal {G}\) by linearly scaling its weight function. We prove this claim here (diameter of graph \(\mathcal {G}\) is represented by \(diam(\mathcal {G})\)):

Lemma 1

For any connected graph \(\mathcal {G}=(V,E,w)\), there is a connected graph \(\mathcal {G}_c=(V,E,w_c)\) such that for every edge \(e\in E\), \(w_c(e)=c\cdot w(e)\) (\(c\in \mathbb {R}^+\)), \(diam(\mathcal {G}_c)=2^h\) (for some \(h\in \mathbb {Z}^+\)), and \(w_c(e)>1\) for every \(e\in E\) [21].

Proof

Consider graph \(\mathcal {G}'=(V,E,w')\) where:

$$\begin{aligned} w'(e)=\dfrac{1+\varepsilon }{\min \limits _{e\in E}\{w(e)\}}\cdot w(e),\quad \forall e\in E, \end{aligned}$$

such that \(\varepsilon \in \mathbb {R}^+\) is an arbitrary positive number. It is clear that \(w'(e)>1\) for every \(e\in E\). We define graph \(\mathcal {G}''=(V,E,w'')\) such that:

$$\begin{aligned} w''(e)=\dfrac{2^{\left\lceil \log _2(diam(\mathcal {G}'))\right\rceil }}{diam(\mathcal {G}')}\cdot w'(e),\quad \forall e\in E. \end{aligned}$$

Since \(w''(e)>1\) and diam\((\mathcal {G}'')=2^{\left\lceil \log _2(diam(\mathcal {G}'))\right\rceil }\), for the following value of c, graph \(\mathcal {G}_c=(V,E,w_c)\) satisfies the conditions mentioned in the Lemma 1.

$$\begin{aligned} c=\dfrac{(1+\varepsilon )2^{\left\lceil \log _2(diam(\mathcal {G}'))\right\rceil }}{diam(\mathcal {G}')\times \min \limits _{e\in E}\{w(e)\}}=2^{\left\lceil \log _2(diam(\mathcal {G}'))\right\rceil }~~\square \end{aligned}$$

\(\square \)

Assuming \(\Delta \) as a positive real number, \(\Delta \)-partition of \(\mathcal {G}\) is defined as a partitionFootnote 4 of V into a number of subsets S such that there exist no pair of nodes in S with distanceFootnote 5 of more than \(\Delta \) from each other.

Hierarchical decomposition sequence (HDS) \(\bar{H}\) of graph \(\mathcal {G}\) is the vector \(\bar{H}=(H_0\), \(H_1\), ..., \(H_h)\) where \(H_h = \{V\}\), \(H_i \text { is a }2^i\text {-partition}\), and if S belongs to \(H_i\), there exists some \( S'\in H_{i+1}\) such that \( S\subseteq S'\) for every \(i=0,1,\ldots ,h-1\). Partition \(H_i\) is called the ith level partition of \(\bar{H}\). Node v is called \(\alpha \)-padded in \(\bar{H}\) (\(0<\alpha <1\)) if for every i, the ith-level partition \(H_{i}\) contains a set \(S\subseteq V\) such that S completely covers ballFootnote 6 \(B_{\mathcal {G}}(v,\alpha \cdot 2^i)\); in other words, for every level i, there exists some \(S\in H_i \text{ such } \text{ that } B_{\mathcal {G}}(v,\alpha \cdot 2^i)\subseteq S \) [21]. See Fig. 8 for illustration.

Fig. 8
figure 8

Schematic view of a padded node (v) in HDS \(\bar{H}=(H_0,H_1,\ldots ,H_3)\) of a weighted graph with diameter 8 [21]. Blue dashed circles denote the balls of center v and different radiuses. The gray circles specify the subsets which belong to partitions in different levels (darker circles belong to lower-level partitions) (color figure online)

In 2003, Fakcharoenphol et al. [50] proposed a randomized algorithm to generate a random HDS in which a minimum quotient of nodes are asymptotically guaranteed to be \(\alpha \)-padded with high probability (\(\alpha \le 1/8\)). The main idea of this algorithm is to construct the ith-level partitions by dividing each \((i+1)^{th}\)-level set \(S\in H_{i+1}\) into smaller subsets \(S_{1}, S_{2},\ldots \) such that there exists no node \(r\in S_{j}\) such that r is further than \(\delta _i\) from an S’s special member called its representative. Value of \(\delta _i\) is chosen randomly in the interval \([2^{i-2},2^{i-1})\). For more details, see Algorithm 1 [21].Footnote 7

figure b

Finally, we define the Hierarchical Decomposition Tree (HDT) which is the main data structure utilized for the novel \(\mathcal {ORED}\) algorithm and is constructed based on the HDS \(\bar{H}\). HDT is an h-level tree where its ith level nodes are the members of \(H_i\) (for every \(i=0,1,\ldots ,h\)) and any ith level tree node \(S\in H_i\) is connected to its parent \(S'\in H_{i+1}\) in upper-level (\(i+1\)) if \(S\subseteq S'\).

Construcing the oblivious routing scheme

First, we show how Algorithm 2 constructs the oblivious routing scheme (mentioned in [21]) which plays the main role in simplification of the couple optimization problems mentioned in Section II by placing more restrictions on the problems, shrinking their feasible areas, and subsequently reducing the computational complexity of them so that their solutions become computationally feasible.

figure c

Algorithm 2 gets the weighted graph \(\mathcal {G}\) as its input and returns function \( \mathbb {S}:\{G_i\}_1^n\times \{F_i\}_1^q\mapsto \mathcal {P}(E) \) which represents the oblivious routing scheme and specifies a pathFootnote 8 in graph \(\mathcal {G}\) for every pair of source-sink nodes. The algorithm starts with generating \(27\log |V|\) random HDS’s utilizing randomized Algorithm 1. The reason for generating multiple random HDS’s is to assure the existence of at-least one HDS \(\bar{H}\) for every pair of nodes \(source\in G\) and \(sink\in F\). In fact, Iyengar et al. in [21] proved that the probability of existing at-least one \(\alpha \)-padding HDS among the \(c\log |V|\) HDS’s constructed by Algorithm 1 is more than \(1-e^{-\frac{(c-2)^2}{2c}}\) (for every \(\alpha \le 1/8\)). By considering \(c\ge 27\), the mentioned probability would be greater than \(1-10^{-5}\) which provides a reasonable theoretical guarantee that Algorithm 2 will find at-least one HDS for every source-sink pair.

After creating \(27\log |V|\) random HDS’s and their corresponding HDTs, Algorithm 2 runs its main loop in lines 5–11 for every pair of source-sink nodes. Assume T as the HDT corresponding to the \(\alpha \)-padding HDS of an arbitrary pair of source-sink nodes. Tree T would have a pair of leaves (level-zero nodes) corresponding to the source and sink (as \(\mathcal {G}\) is supposed to be a weighted graph of the weight function greater than one for every edge). Let p denote the only tree path connecting the mentioned leaves together. Finally, the resulted routing scheme is computed in line 11 where the path between source and sink nodes \(\mathbb {S}(source,sink)\) is obtained by projecting p on graph \(\mathcal {G}\). The projection of path p of HDT T on graph \(\mathcal {G}\) is defined in the following way:

Consider \(\gamma _e\) as the shortest path between the incident nodes of arbitrary edge \(e\in p\) in graph \(\mathcal {G}\). The projection of tree path p on the graph is obtained by concatenating all of the shortest paths \(\gamma _e\)s back to back. In the case that the concatenation result is not a simple path and has crossed some nodes more than once, the projected path will be the shortest simple path corresponding to the concatenation result.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Boroojeni, K.G., Amini, M.H., Iyengar, S.S. et al. An economic dispatch algorithm for congestion management of smart power networks. Energy Syst 8, 643–667 (2017). https://doi.org/10.1007/s12667-016-0224-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12667-016-0224-6

Keywords

  • Congestion management
  • Economic dispatch
  • Loss minimization
  • Oblivious network design
  • Smart power network