Generation Scheduling with Integration of Wind Power and Compressed Air Energy Storage (original) (raw)
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Security Constrained Unit Commitment with Wind Generation and Compressed Air Energy Storage
Wind power is one of the fastest growing renewable sources of generation in the U.S. and many other countries. As wind-generated electricity continues to grow, electric utilities increasingly grapple with the challenges of connecting that power to the grid although maintaining system security. It is difficult to predict and control the output of wind generation because of wind intermittency and a reserve capacity is required to deal with inherent uncertainty. This study presents an approach for securityconstrained unit commitment (SCUC) with integration of an energy storage system (ESS) and wind generation. Compressed air energy storage (CAES) is considered as an alternative solution to store energy. For economical operation and control purposes, utilities with CAES are interested in the availability and the dispatch of CAES on an hourly basis, given the specific characteristics of CAES. The main contribution of this study is the development of enhanced SCUC formulation and solution techniques with wind power, CAES and multiple constraints including fuel and emission limit. Proposed approach allows simultaneous optimisation of the energy and the ancillary services (AS). Case studies with eight-bus and 118-bus systems are presented to validate the proposed model. This study also contributes by conducting comprehensive studies to analyse the impact of CAES system on locational pricing, economics, peak-load shaving, transmission congestion management, wind curtailment and environmental perspective.
Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES)
2013
This study optimises and compares the operation of a conventional gas-fired power generation company with its operation in combination with wind power and compressed air energy storage (CAES). A mixed integer non-linear programming (MINLP) formulation is developed for the optimisation problem. Limits in ramp rate, capacity, and minimum on/off time, as well as start-up cost constraints, are considered for the modelling of conventional units. Injected and produced power constraints, storage, air balance and CAES-operation limits are considered in the CAES modelling. Two objective functions (profit maximisation and cost minimisation) are modelled. Without considering capital costs, it is found that the use of CAES results in 43% higher operational profits and 6.7% lower costs in a market environment.
With the massive deployment of renewable generation resources especially wind power generation in many electricity markets, the intermittency issues related to these resources must be addressed. As a possible solution, energy storage systems (ESS) can be considered to cope with wind power variability and support its integration. In this paper, compressed air energy storage (CAES) is employed to help integrate a high penetration of wind generation into the electricity market. To this end, a stochastic market-clearing model considering wind generation uncertainty is presented. Case studies in a three-bus test system are conducted to demonstrate the CAES effectiveness for the wind power integration. The results indicate that the operation of CAES in wind-thermal generating systems can be very beneficial for the electricity market performance from the economical and operational viewpoints.
Compressed Air Energy Storage-Part II: Application to Power System Unit Commitment
arXiv: Signal Processing, 2017
Unit commitment (UC) is one of the most important power system operation problems. To integrate higher penetration of wind power into power systems, more compressed air energy storage (CAES) plants are being built. Existing cavern models for the CAES used in power system optimization problems are not accurate, which may lead to infeasible solutions, e.g., the air pressure in the cavern is outside its operating range. In this regard, an accurate CAES model is proposed for the UC problem based on the accurate bi-linear cavern model proposed in the first paper of this two-part series. The minimum switch time between the charging and discharging processes of CAES is considered. The whole model, i.e., the UC model with an accurate CAES model, is a large-scale mixed integer bi-linear programming problem. To reduce the complexity of the whole model, three strategies are proposed to reduce the number of bi-linear terms without sacrificing accuracy. McCormick relaxation and piecewise lineari...
2018
This paper introduces a new approach for scheduling security constraint unit commitment (SCUC) including wind farms. Because of uncertainty in wind power production, we tried to develop a new method for incorporating wind power generation in power plant scheduling. For this, wind power generation modeled with unit commitment in a nonlinear optimization problem and simulated by submitting different scenarios for wind farms. First, unit commitment solved in master problem. Then, scenarios for presenting volatile nature of wind power simulated. Numerical simulations show the effectiveness of supposed unit commitment for managing security of power system by considering volatility of wind power generation.
IEEE Transactions on Sustainable Energy
The uncertainty of wind energy makes wind power producers (WPPs) incur profit loss due to balancing costs in electricity markets, a phenomenon that restricts their participation in markets. This paper proposes a stochastic bidding strategy based on virtual power plants (VPPs) to increase the profit of WPPs in short-term electricity markets in coordination with energy storage systems (ESSs) and demand response (DR). To implement the stochastic solution strategy, the Kantorovich method is used for scenario generation and reduction. The optimization problem is formulated as a Mixed-Integer Linear Programming (MILP) problem. From testing the proposed method for a Spanish WPP, it is inferred that the proposed method enhances the profit of the VPP compared to previous models.
Impact of Battery Energy Storage on Power System with High Wind Penetration
The penetration of renewable resources in power system has been increasing in recent years. Many of these resources are uncontrollable and variable in nature, wind in particular, are relatively unpredictable. At high penetration levels, volatility of wind power production could cause problems for power system to maintain system security and reliability. One of the solutions being proposed to improve reliability and performance of the system is to integrate energy storage devices into the network. In this paper, unit commitment and dispatch schedule in power system with and without energy storage is examined for different level of wind penetration. Battery energy storage (BES) is considered as an alternative solution to store energy. The SCUC formulation and solution technique with wind power and BES is presented. The proposed formulation and model is validated with eight-bus system case study. Further, a discussion on the role of BES on locational pricing, economic, peak load shaving, and transmission congestion management had been made.
2016
Increasing the penetration of variable wind generation in power systems has created some new challenges in the power system operation. In such a situation, the inclusion of flexible resources which have the potential of facilitating wind power integration is necessary. Demand response (DR) programs and emerging utility-scale energy storages (ESs) are known as two powerful flexible tools that can improve large-scale integration of intermittent wind power from technical and economic aspects. Under this perspective, this paper proposes a multi objective stochastic framework that schedules conventional generation units, bulk ESs, and DR resources simultaneously with the application to wind integration. The proposed formulation is a sophisticated problem which coordinates supply-side and demand-side resources in energy and up/down spinning reserve markets so that the cost, emission, and multi objective functions are minimized separately. In order to determine the most efficient DR progra...
Applied Energy, 2011
Competitive structure of power markets causes various challenges for wind resources to participate in these markets. Indeed, production uncertainty is the main cause of their low income. Thus, they are usually supported by system operators, which is in contrast with the competitive paradigm of power markets. In this paper, a new strategy for increasing the profits of wind resources is proposed. In the suggested strategy, a Generation Company (GenCo), who owns both wind and pumped-storage plants, self-schedules the integrated operation of them regarding the uncertainty of wind power generation. For presenting an integrated self-schedule and obtaining a real added value of the strategy, participation of the GenCo in energy and ancillary service markets is modeled. The self-scheduling strategy is based on stochastic programming techniques. Outputs of the problem include generation offers in day-ahead energy market and ancillary service markets, including spinning and regulation reserve markets. A Neural Network (NN) based technique is used for modeling the uncertainty of wind power production. The proposed strategy is tested on a real wind farm in mainland, Spain. Moreover, added value of the strategy is presented in different conditions of the market.