On decentralized convex optimization in a multi-agent setting with separable constraints and its application to optimal charging of electric vehicles
Summary (2 min read)
I. INTRODUCTION
- Optimization in multi-agent systems has attracted significant attention in the control and operations research communities, due to its applicability to different domains, e.g., energy systems, mobility systems, robotic networks, etc.
- The considered structure, although specific, captures a wide class of engineering problems, like the electric vehicle optimal charging problem studied in this paper.
- The authors follow an iterative algorithm, where at every iteration each agent solves a local optimization problem with respect to its own local decision Research was supported by the European Commission, H2020, under the project UnCoVerCPS, grant number 643921.
- The second direction involves mainly the so called Jacobi algorithm, which serves as an alternative to gradient algorithms.
II. PROBLEM STATEMENT
- The authors impose the following assumption throughout the paper.
- Note, however, that P does not necessarily admit a unique minimizer.
- This allows to cope with privacy and computational issues.
- Note that under Assumption 1, and due to the presence of the quadratic penalty term, the resulting problem is strictly convex with respect to z i , and hence admits a unique minimizer.
B. Connections between minimizers and fixed-points
- The authors report here a fundamental optimality result (e.g., see Proposition 3.1 in [1] ), that they will often use in the sequel.
- The proofs are omitted due to space limitation, they can be found in [13, Proposition 2 and 3].
- Note that the connection between minimizers and fixedpoints, similar to the ones in Proposition 2, has been also investigated in [14] , in the context of Nash equilibria in noncooperative games.
IV. MAIN CONVERGENCE RESULT
- And focus on convex optimization problems with a convex, quadratic objective function.the authors.
- Notice the slight abuse of notation in (8) , where the weighted identity matrix I c in the second and the third equality are not of the same dimension.
- In the next proposition the non-expansive property of the mapping T is proven.
- EQUATION ) where the last inequality follows after some algebraic calculations and the fact that Q and Q d +.
- Moreover, the results of Section II are valid for any convex objective function, not necessarily quadratic, thus opening the road for extending the convergence results of Section III by relaxing Assumption 2; see [13] for preliminary results.
A. Alternative implementations
- The authors now investigate conditions under which T is firmly non-expansive.
- The second equality is due to the definition ξ, and the last one follows after performing computation.
B. Information exchange
- The required amount of information that needs to be exchanged can be significantly reduced when the objective function exhibits some particular structure.
- This is the case, e.g., for objective functions that are coupled only through the average of some variables.
- The central authority needs then to collect the solutions of all agents, but it only has to broadcast the average value.
V. OPTIMAL CHARGING OF ELECTRIC VEHICLES
- The objective function in (23) encodes the total electricity cost given by the demand (both PEVs and non-PEVs) multiplied by the price of electricity, which in turn depends linearly on the total demand through p t , thus giving rise to the quadratic function in (23).
- This linear dependency of price with respect to the total demand models the fact that agents/vehicles are price anticipating authorities, anticipating their consumption to have an effect on the electricity price (see [17] ).
A. Simulation results
- All optimization problems are solved using CPLEX, [18] .
- All the other parameters are left unchanged with respect to the previous set-up.
- Figure 2 depicts the PEV, non-PEV, and total demand along the considered time horizon.
VI. CONCLUDING REMARKS
- A decentralized algorithm for multi-agent, convex optimization programs with a common objective and subject to separable constraints, was developed.
- For the case where the objective function is quadratic, the proposed scheme was shown to converge to some minimizer of the centralized problem, and its performance was illustrated by applying it to the problem of optimal charging of electric vehicles.
- Current work concentrates on relaxing Assumption 2, extending their convergence results to the case of contin- uously differentiable, convex, but not necessarily quadratic, objective functions (see [13] ).
Did you find this useful? Give us your feedback
Citations
34 citations
26 citations
25 citations
15 citations
12 citations
References
91 citations
"On decentralized convex optimizatio..." refers background in this paper
..., optimal power flow type of problems [10], optimal charging of electric vehicles [11], [12], etc....
[...]
68 citations
"On decentralized convex optimizatio..." refers background in this paper
...This linear dependency of price with respect to the total demand models the fact that agents/vehicles are price anticipating authorities, anticipating their consumption to have an effect on the electricity price (see [17])....
[...]
21 citations
"On decentralized convex optimizatio..." refers methods in this paper
...3) We apply the proposed algorithm to the problem of optimal charging of electric vehicles, extending the results of [9], [11], [12], and achieving convergence to an optimal charging scheme with a finite number of vehicles....
[...]
...The deterministic, discrete-time problem variant, accounting for the presence of separable constraints, is treated using fixed-point theoretic tools in [9]....
[...]
...We follow the formulation of [9], [11], [12]; but, our algorithm converges to a minimizer of the centralized problem counterpart and with a finite number of agents/vehicles, as opposed to the aforementioned references, where convergence to a Nash equilibrium at the limiting case of an infinite population of agents is established....
[...]
...Motivated by the analysis of [9], we have that...
[...]
...Our analysis, however, not only complements the scaled gradient projection algorithm by constituting its Jacobi-like equivalent without requiring strict convexity of the objective function as it is usually the case for the standard Jacobi algorithm, but also follows a different analysis from that in [1], motivated by the fixed-point theoretic results of [9]....
[...]
3 citations
"On decentralized convex optimizatio..." refers background or result in this paper
...A more detailed numerical investigation can be found in [13]....
[...]
...Moreover, the results of Section II are valid for any convex objective function, not necessarily quadratic, thus opening the road for extending the convergence results of Section III by relaxing Assumption 2; see [13] for preliminary results....
[...]
...Current work concentrates on relaxing Assumption 2, extending our convergence results to the case of continuously differentiable, convex, but not necessarily quadratic, objective functions (see [13])....
[...]