199 research outputs found
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
Moving from Linear to Conic Markets for Electricity
We propose a new forward electricity market framework that admits
heterogeneous market participants with second-order cone strategy sets, who
accurately express the nonlinearities in their costs and constraints through
conic bids, and a network operator facing conic operational constraints. In
contrast to the prevalent linear-programming-based electricity markets, we
highlight how the inclusion of second-order cone constraints enables
uncertainty-, asset- and network-awareness of the market, which is key to the
successful transition towards an electricity system based on weather-dependent
renewable energy sources. We analyze our general market-clearing proposal using
conic duality theory to derive efficient spatially-differentiated prices for
the multiple commodities, comprising of energy and flexibility services. Under
the assumption of perfect competition, we prove the equivalence of the
centrally-solved market-clearing optimization problem to a competitive spatial
price equilibrium involving a set of rational and self-interested participants
and a price setter. Finally, under common assumptions, we prove that moving
towards conic markets does not incur the loss of desirable economic properties
of markets, namely market efficiency, cost recovery and revenue adequacy. Our
numerical studies focus on the specific use case of uncertainty-aware market
design and demonstrate that the proposed conic market brings advantages over
existing alternatives within the linear programming market framework.Comment: Manuscript with electronic companion; submitted to Operations
Researc
An Efficient Primal-Dual Approach to Chance-Constrained Economic Dispatch
To effectively enhance the integration of distributed and renewable energy
sources in future smart microgrids, economical energy management accounting for
the principal challenge of the variable and non-dispatchable renewables is
indispensable and of significant importance. Day-ahead economic generation
dispatch with demand-side management for a microgrid in islanded mode is
considered in this paper. With the goal of limiting the risk of the
loss-of-load probability, a joint chance constrained optimization problem is
formulated for the optimal multi-period energy scheduling with multiple wind
farms. Bypassing the intractable spatio-temporal joint distribution of the wind
power generation, a primal-dual approach is used to obtain a suboptimal
solution efficiently. The method is based on first-order optimality conditions
and successive approximation of the probabilistic constraint by generation of
p-efficient points. Numerical results are reported to corroborate the merits of
this approach.Comment: Appeared in 2014 North American Power Symposiu
Simultaneous Minimization of Energy Losses and Greenhouse Gas Emissions in AC Distribution Networks Using BESS
The problem of the optimal operation of battery energy storage systems (BESSs) in AC grids is addressed in this paper from the point of view of multi-objective optimization. A nonlinear programming (NLP) model is presented to minimize the total emissions of contaminant gasses to the atmosphere and costs of daily energy losses simultaneously, considering the AC grid complete model. The BESSs are modeled with their linear relation between the state-of-charge and the active power injection/absorption. The Pareto front for the multi-objective optimization NLP model is reached through the general algebraic modeling system, i.e., GAMS, implementing the pondered optimization approach using weighting factors for each objective function. Numerical results in the IEEE 33-bus and IEEE 69-node test feeders demonstrate the multi-objective nature of this optimization problem and the multiple possibilities that allow the grid operators to carry out an efficient operation of their distribution networks when BESS and renewable energy resources are introduced.Universidad TecnolĂłgica de BolĂva
- …