38 research outputs found
Energy Storage Sharing Strategy in Distribution Networks Using Bi-level Optimization Approach
In this paper, we address the energy storage management problem in
distribution networks from the perspective of an independent energy storage
manager (IESM) who aims to realize optimal energy storage sharing with
multi-objective optimization, i.e., optimizing the system peak loads and the
electricity purchase costs of the distribution company (DisCo) and its
customers. To achieve the goal of the IESM, an energy storage sharing strategy
is therefore proposed, which allows DisCo and customers to control the assigned
energy storage. The strategy is updated day by day according to the system
information change. The problem is formulated as a bi-level mathematical model
where the upper level model (ULM) seeks for optimal division of energy storage
among Disco and customers, and the lower level models (LLMs) represent the
minimizations of the electricity purchase costs of DisCo and customers.
Further, in order to enhance the computation efficiency, we transform the
bi-level model into a single-level mathematical program with equilibrium
constraints (MPEC) model and linearize it. Finally, we validate the
effectiveness of the strategy and complement our analysis through case studies
Distributed Online Modified Greedy Algorithm for Networked Storage Operation under Uncertainty
The integration of intermittent and stochastic renewable energy resources
requires increased flexibility in the operation of the electric grid. Storage,
broadly speaking, provides the flexibility of shifting energy over time;
network, on the other hand, provides the flexibility of shifting energy over
geographical locations. The optimal control of storage networks in stochastic
environments is an important open problem. The key challenge is that, even in
small networks, the corresponding constrained stochastic control problems on
continuous spaces suffer from curses of dimensionality, and are intractable in
general settings. For large networks, no efficient algorithm is known to give
optimal or provably near-optimal performance for this problem. This paper
provides an efficient algorithm to solve this problem with performance
guarantees. We study the operation of storage networks, i.e., a storage system
interconnected via a power network. An online algorithm, termed Online Modified
Greedy algorithm, is developed for the corresponding constrained stochastic
control problem. A sub-optimality bound for the algorithm is derived, and a
semidefinite program is constructed to minimize the bound. In many cases, the
bound approaches zero so that the algorithm is near-optimal. A task-based
distributed implementation of the online algorithm relying only on local
information and neighbor communication is then developed based on the
alternating direction method of multipliers. Numerical examples verify the
established theoretical performance bounds, and demonstrate the scalability of
the algorithm.Comment: arXiv admin note: text overlap with arXiv:1405.778
Optimization of stochastic lossy transport networks and applications to power grids
Motivated by developments in renewable energy and smart grids, we formulate a
stylized mathematical model of a transport network with stochastic load
fluctuations. Using an affine control rule, we explore the trade-off between
the number of controllable resources in a lossy transport network and the
performance gain they yield in terms of expected power losses. Our results are
explicit and reveal the interaction between the level of flexibility, the
intrinsic load uncertainty and the network structure.Comment: 30 pages, 10 figure
Online Modified Greedy Algorithm for Storage Control under Uncertainty
This paper studies the general problem of operating energy storage under
uncertainty. Two fundamental sources of uncertainty are considered, namely the
uncertainty in the unexpected fluctuation of the net demand process and the
uncertainty in the locational marginal prices. We propose a very simple
algorithm termed Online Modified Greedy (OMG) algorithm for this problem. A
stylized analysis for the algorithm is performed, which shows that comparing to
the optimal cost of the corresponding stochastic control problem, the
sub-optimality of OMG is bounded and approaches zero in various scenarios. This
suggests that, albeit simple, OMG is guaranteed to have good performance in
some cases; and in other cases, OMG together with the sub-optimality bound can
be used to provide a lower bound for the optimal cost. Such a lower bound can
be valuable in evaluating other heuristic algorithms. For the latter cases, a
semidefinite program is derived to minimize the sub-optimality bound of OMG.
Numerical experiments are conducted to verify our theoretical analysis and to
demonstrate the use of the algorithm.Comment: 14 page version of a paper submitted to IEEE trans on Power System