13 research outputs found
Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market
The deregulation of electricity markets has
diversified the range of financial transaction modes between
independent system operator (ISO), generation companies
(GENCO) and load-serving entities (LSE) as the main
interacting players of a day-ahead market (DAM). LSEs sell
electricity to end-users and retail customers. The LSE that owns
distributed generation (DG) or energy storage units can supply
part of its serving loads when the nodal price of electricity rises.
This opportunity stimulates them to have storage or generation
facilities at the buses with higher locational marginal prices
(LMP). The short-term advantage of this model is reducing the
risk of financial losses for LSEs in DAMs and its long-term
benefit for the LSEs and the whole system is market power
mitigation by virtually increasing the price elasticity of demand.
This model also enables the LSEs to manage the financial risks
with a stochastic programming framework
Cooperation and Storage Tradeoffs in Power-Grids with Renewable Energy Resources
One of the most important challenges in smart grid systems is the integration
of renewable energy resources into its design. In this work, two different
techniques to mitigate the time varying and intermittent nature of renewable
energy generation are considered. The first one is the use of storage, which
smooths out the fluctuations in the renewable energy generation across time.
The second technique is the concept of distributed generation combined with
cooperation by exchanging energy among the distributed sources. This technique
averages out the variation in energy production across space. This paper
analyzes the trade-off between these two techniques. The problem is formulated
as a stochastic optimization problem with the objective of minimizing the time
average cost of energy exchange within the grid. First, an analytical model of
the optimal cost is provided by investigating the steady state of the system
for some specific scenarios. Then, an algorithm to solve the cost minimization
problem using the technique of Lyapunov optimization is developed and results
for the performance of the algorithm are provided. These results show that in
the presence of limited storage devices, the grid can benefit greatly from
cooperation, whereas in the presence of large storage capacity, cooperation
does not yield much benefit. Further, it is observed that most of the gains
from cooperation can be obtained by exchanging energy only among a few energy
harvesting sources
Privacy-preserving Energy Scheduling for Smart Grid with Renewables
We consider joint demand response and power procurement to optimize the average social welfare of a smart power grid system with renewable sources. The renewable sources such as wind and solar energy are intermittent and fluctuate rapidly. As a consequence, the demand response algorithm needs to be executed in real time to ensure the stability of a smart grid system with renewable sources. We develop a demand response algorithm that converges to the optimal solution with superlinear rates of convergence. In the simulation studies, the proposed algorithm converges roughly thirty time faster than the traditional subgradient algorithm. In addition, it is fully distributed and can be realized either synchronously or in asynchronous manner, which eases practical deployment
Harnessing Flexible and Reliable Demand Response Under Customer Uncertainties
Demand response (DR) is a cost-effective and environmentally friendly
approach for mitigating the uncertainties in renewable energy integration by
taking advantage of the flexibility of customers' demands. However, existing DR
programs suffer from either low participation due to strict commitment
requirements or not being reliable in voluntary programs. In addition, the
capacity planning for energy storage/reserves is traditionally done separately
from the demand response program design, which incurs inefficiencies. Moreover,
customers often face high uncertainties in their costs in providing demand
response, which is not well studied in literature.
This paper first models the problem of joint capacity planning and demand
response program design by a stochastic optimization problem, which
incorporates the uncertainties from renewable energy generation, customer power
demands, as well as the customers' costs in providing DR. We propose online DR
control policies based on the optimal structures of the offline solution. A
distributed algorithm is then developed for implementing the control policies
without efficiency loss. We further offer enhanced policy design by allowing
flexibilities into the commitment level. We perform real world trace based
numerical simulations. Results demonstrate that the proposed algorithms can
achieve near optimal social costs, and significant social cost savings compared
to baseline methods