7,614 research outputs found
Operational planning and bidding for district heating systems with uncertain renewable energy production
In countries with an extended use of district heating (DH), the integrated
operation of DH and power systems can increase the flexibility of the power
system achieving a higher integration of renewable energy sources (RES). DH
operators can not only provide flexibility to the power system by acting on the
electricity market, but also profit from the situation to lower the overall
system cost. However, the operational planning and bidding includes several
uncertain components at the time of planning: electricity prices as well as
heat and power production from RES. In this publication, we propose a planning
method that supports DH operators by scheduling the production and creating
bids for the day-ahead and balancing electricity markets. The method is based
on stochastic programming and extends bidding strategies for virtual power
plants to the DH application. The uncertain factors are considered explicitly
through scenario generation. We apply our solution approach to a real case
study in Denmark and perform an extensive analysis of the production and
trading behaviour of the DH system. The analysis provides insights on how DH
system can provide regulating power as well as the impact of uncertainties and
renewable sources on the planning. Furthermore, the case study shows the
benefit in terms of cost reductions from considering a portfolio of units and
both markets to adapt to RES production and market states
An Integrated Market for Electricity and Natural Gas Systems with Stochastic Power Producers
In energy systems with high shares of weather-driven renewable power sources,
gas-fired power plants can serve as a back-up technology to ensure security of
supply and provide short-term flexibility. Therefore, a tighter coordination
between electricity and natural gas networks is foreseen. In this work, we
examine different levels of coordination in terms of system integration and
time coupling of trading floors. We propose an integrated operational model for
electricity and natural gas systems under uncertain power supply by applying
two-stage stochastic programming. This formulation co-optimizes day-ahead and
real-time dispatch of both energy systems and aims at minimizing the total
expected cost. Additionally, two deterministic models, one of an integrated
energy system and one that treats the two systems independently, are presented.
We utilize a formulation that considers the linepack of the natural gas system,
while it results in a tractable mixed-integer linear programming (MILP) model.
Our analysis demonstrates the effectiveness of the proposed model in
accommodating high shares of renewables and the importance of proper natural
gas system modeling in short-term operations to reveal valuable flexibility of
the natural gas system. Moreover, we identify the coordination parameters
between the two markets and show their impact on the system's operation and
dispatch
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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A Dynamic Approach to Estimate the Efficiency of U.S. Electric Utilities
The static production efficiency model and the dynamic duality model of intertemporal decision making using a parametric approach have been continuously developed but in separate direction. The parametric approach takes statistical noise into account, which consequently provides accurate measures in a stochastic environment. In this study the static shadow cost approach and the dynamic duality model of intertemporal decision making are integrated to formulate theoretical and econometric models of dynamic efficiency with intertemporal cost minimizing firm behavior. The dynamic efficiency model is a dynamic measure of firms’ inefficiency and it accounts for allocative and technical inefficiencies of net investment and of variable inputs. The dynamic efficiency model is implemented by using the Generalized Method of Moment (GMM) estimation and empirically applied into a panel data set of 72 U.S. major investor-owned electric utilities using fossil-fuel fired steam electric power generation during the time period of 1986 to 1999. The major results of this study are that most electric utilities in this study underutilized fuel relative to the aggregated labor and maintenance input and they overutilized capital in production. The estimates of the input price elasticities present the substitution possibilities among the inputs. Finally, the results suggest evidence of increasing returns to scale in the production of the electricity industryEfficiency, GMM estimation, shadow cost approach, dynamic duality, deregulation, electricity
Dynamic Efficiency Estimation: An Application to US Electric Utilities
The static production efficiency model and the dynamic duality model of intertemporal decision making using a parametric approach have been continuously developed but in separate direction. In this study the static shadow cost approach and the dynamic duality model of intertemporal decision making are integrated to formulate theoretical and econometric models of dynamic efficiency with intertemporal cost minimizing firm behavior. The dynamic efficiency model is empirically implemented using a panel data set of 72 U.S. major investor-owned electric utilities using fossil-fuel fired steam electric power generation during the time period of 1986 to 1999. The major results of this study are that most electric utilities in this study underutilized fuel relative to the aggregated labor and maintenance input and they overutilized capital in production. Electric utilities with relatively high technical inefficiency of variable inputs demand in production in states adopting a deregulation plan improve the performance of the utilities. The estimates of the input price elasticities present the substitution possibilities among the inputs. Finally, the results suggest evidence of increasing returns to scale in the production of the electricity industry.
Investigation on electricity market designs enabling demand response and wind generation
Demand Response (DR) comprises some reactions taken by the end-use customers to decrease
or shift the electricity consumption in response to a change in the price of electricity or a
specified incentive payment over time. Wind energy is one of the renewable energies which
has been increasingly used throughout the world. The intermittency and volatility of
renewable energies, wind energy in particular, pose several challenges to Independent
System Operators (ISOs), paving the way to an increasing interest on Demand Response
Programs (DRPs) to cope with those challenges. Hence, this thesis addresses various
electricity market designs enabling DR and Renewable Energy Systems (RESs) simultaneously.
Various types of DRPs are developed in this thesis in a market environment, including
Incentive-Based DR Programs (IBDRPs), Time-Based Rate DR Programs (TBRDRPs) and
combinational DR programs on wind power integration. The uncertainties of wind power
generation are considered through a two-stage Stochastic Programming (SP) model. DRPs are
prioritized according to the ISO’s economic, technical, and environmental needs by means of
the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The
impacts of DRPs on price elasticity and customer benefit function are addressed, including
the sensitivities of both DR parameters and wind power scenarios. Finally, a two-stage
stochastic model is applied to solve the problem in a mixed-integer linear programming (MILP)
approach. The proposed model is applied to a modified IEEE test system to demonstrate the
effect of DR in the reduction of operation cost.A Resposta Dinâmica dos Consumidores (DR) compreende algumas reações tomadas por estes
para reduzir ou adiar o consumo de eletricidade, em resposta a uma mudança no preço da
eletricidade, ou a um pagamento/incentivo específico. A energia eólica é uma das energias
renováveis que tem sido cada vez mais utilizada em todo o mundo. A intermitência e a
volatilidade das energias renováveis, em particular da energia eólica, acarretam vários
desafios para os Operadores de Sistema (ISOs), abrindo caminho para um interesse crescente
nos Programas de Resposta Dinâmica dos Consumidores (DRPs) para lidar com esses desafios.
Assim, esta tese aborda os mercados de eletricidade com DR e sistemas de energia renovável
(RES) simultaneamente. Vários tipos de DRPs são desenvolvidos nesta tese em ambiente de
mercado, incluindo Programas de DR baseados em incentivos (IBDRPs), taxas baseadas no
tempo (TBRDRPs) e programas combinados (TBRDRPs) na integração de energia eólica. As
incertezas associadas à geração eólica são consideradas através de um modelo de
programação estocástica (SP) de dois estágios. Os DRPs são priorizados de acordo com as
necessidades económicas, técnicas e ambientais do ISO por meio da técnica para ordem de
preferência por similaridade com a solução ideal (TOPSIS). Os impactes dos DRPs na
elasticidade do preço e na função de benefício ao cliente são abordados, incluindo as
sensibilidades dos parâmetros de DR e dos cenários de potência eólica. Finalmente, um
modelo estocástico de dois estágios é aplicado para resolver o problema numa abordagem de
programação linear inteira mista (MILP). O modelo proposto é testado num sistema IEEE
modificado para demonstrar o efeito da DR na redução do custo de operação
A robust optimisation approach using CVaR for unit commitment in a market with probabilistic offers
The large scale integration of renewable energy sources (RES) challenges power system planners and operators alike as it can potentially introduce the need for costly investments in infrastructure. Furthermore, traditional market clearing mechanisms are no longer optimal due to the stochastic nature of RES. This paper presents a risk-aware market clearing strategy for a network with significant shares of RES.We propose an electricity market that embeds the uncertainty brought by wind power and other stochastic renewable sources by accepting probabilistic offers and use a risk measure defined by conditional value-at-risk (CVaR) to evaluate the risk of high re-dispatching cost due to the mis-estimation of renewable energy. The proposed model is simulated on a 39-bus network, whereby it is shown that significant reductions can be achieved by properly managing the risks of mis-estimation of stochastic generation
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