2,577 research outputs found
Risk-Aware Management of Distributed Energy Resources
High wind energy penetration critically challenges the economic dispatch of
current and future power systems. Supply and demand must be balanced at every
bus of the grid, while respecting transmission line ratings and accounting for
the stochastic nature of renewable energy sources. Aligned to that goal, a
network-constrained economic dispatch is developed in this paper. To account
for the uncertainty of renewable energy forecasts, wind farm schedules are
determined so that they can be delivered over the transmission network with a
prescribed probability. Given that the distribution of wind power forecasts is
rarely known, and/or uncertainties may yield non-convex feasible sets for the
power schedules, a scenario approximation technique using Monte Carlo sampling
is pursued. Upon utilizing the structure of the DC optimal power flow (OPF), a
distribution-free convex problem formulation is derived whose complexity scales
well with the wind forecast sample size. The efficacy of this novel approach is
evaluated over the IEEE 30-bus power grid benchmark after including real
operation data from seven wind farms.Comment: To appear in Proc. of 18th Intl. Conf. on DSP, Santorini Island,
Greece, July 1-3, 201
Robust Optimal Power Flow with Wind Integration Using Conditional Value-at-Risk
Integrating renewable energy into the power grid requires intelligent
risk-aware dispatch accounting for the stochastic availability of renewables.
Toward achieving this goal, a robust DC optimal flow problem is developed in
the present paper for power systems with a high penetration of wind energy. The
optimal dispatch is obtained as the solution to a convex program with a
suitable regularizer, which is able to mitigate the potentially high risk of
inadequate wind power. The regularizer is constructed based on the energy
transaction cost using conditional value-at-risk (CVaR). Bypassing the
prohibitive high-dimensional integral, the distribution-free sample average
approximation method is efficiently utilized for solving the resulting
optimization problem. Case studies are reported to corroborate the efficacy of
the novel model and approach tested on the IEEE 30-bus benchmark system with
real operation data from seven wind farms.Comment: To Appear in Proc. of the 4th Intl. Conf. on Smart Grid
Communication
Distributed Stochastic Market Clearing with High-Penetration Wind Power
Integrating renewable energy into the modern power grid requires
risk-cognizant dispatch of resources to account for the stochastic availability
of renewables. Toward this goal, day-ahead stochastic market clearing with
high-penetration wind energy is pursued in this paper based on the DC optimal
power flow (OPF). The objective is to minimize the social cost which consists
of conventional generation costs, end-user disutility, as well as a risk
measure of the system re-dispatching cost. Capitalizing on the conditional
value-at-risk (CVaR), the novel model is able to mitigate the potentially high
risk of the recourse actions to compensate wind forecast errors. The resulting
convex optimization task is tackled via a distribution-free sample average
based approximation to bypass the prohibitively complex high-dimensional
integration. Furthermore, to cope with possibly large-scale dispatchable loads,
a fast distributed solver is developed with guaranteed convergence using the
alternating direction method of multipliers (ADMM). Numerical results tested on
a modified benchmark system are reported to corroborate the merits of the novel
framework and proposed approaches.Comment: To appear in IEEE Transactions on Power Systems; 12 pages and 9
figure
Reducing Voltage Volatility with Step Voltage Regulators: A Life-Cycle Cost Analysis of Korean Solar Photovoltaic Distributed Generation
To meet the United Nation’s sustainable development energy goal, the Korean Ministry of Commerce announced they would increase renewable energy generation to 5.3% by 2029. These energy sources are often produced in small-scale power plants located close to the end users, known as distributed generation (DG). The use of DG is an excellent way to reduce greenhouse gases but has also been found to reduce power quality and safety reliability through an increase in voltage volatility. This paper performs a life-cycle cost analysis on the use of step voltage regulators (SVR) to reduce said volatility, simulating the impact they have on existing Korean solar photovoltaic (PV) DG. From the data collected on a Korean Electrical Power Corporation 30 km/8.2 megawatts (MW) feeder system, SVRs were found to increase earnings by one million USD. SVR volatile voltage mitigation increased expected earnings by increasing the estimated allowable PV power generation by 2.7 MW. While this study is based on Korean PV power generation, its findings are applicable to any DG sources worldwide.11Nsciescopu
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
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