99,070 research outputs found
Review of trends and targets of complex systems for power system optimization
Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107
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
A Multiperiod OPF Model Under Renewable Generation Uncertainty and Demand Side Flexibility
Renewable energy sources such as wind and solar have received much attention
in recent years and large amount of renewable generation is being integrated to
the electricity networks. A fundamental challenge in power system operation is
to handle the intermittent nature of the renewable generation. In this paper we
present a stochastic programming approach to solve a multiperiod optimal power
flow problem under renewable generation uncertainty. The proposed approach
consists of two stages. In the first stage operating points for conventional
power plants are determined. Second stage realizes the generation from
renewable resources and optimally accommodates it by relying on demand-side
flexibility. The benefits from its application are demonstrated and discussed
on a 4-bus and a 39-bus systems. Numerical results show that with limited
flexibility on the demand-side substantial benefits in terms of potential
additional re-dispatch costs can be achieved. The scaling properties of the
approach are finally analysed based on standard IEEE test cases upto 300 buses,
allowing to underlined its computational efficiency.Comment: 8 pages, 10 figure
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
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