Civil and Environmental Engineeringhttp://hdl.handle.net/10468/1942015-10-13T18:28:27Z2015-10-13T18:28:27ZQuantifying the value of improved wind energy forecasts in a pool-based electricity marketMcGarrigle, Edward V.Leahy, Paul G.http://hdl.handle.net/10468/17922015-05-06T11:47:09Z2015-08-01T00:00:00ZQuantifying the value of improved wind energy forecasts in a pool-based electricity market
McGarrigle, Edward V.; Leahy, Paul G.
This work illustrates the influence of wind forecast errors on system costs, wind curtailment and generator dispatch in a system with high wind penetration. Realistic wind forecasts of different specified accuracy levels are created using an auto-regressive moving average model and these are then used in the creation of day-ahead unit commitment schedules. The schedules are generated for a model of the 2020 Irish electricity system with 33% wind penetration using both stochastic and deterministic approaches. Improvements in wind forecast accuracy are demonstrated to deliver: (i) clear savings in total system costs for deterministic and, to a lesser extent, stochastic scheduling; (ii) a decrease in the level of wind curtailment, with close agreement between stochastic and deterministic scheduling; and (iii) a decrease in the dispatch of open cycle gas turbine generation, evident with deterministic, and to a lesser extent, with stochastic scheduling.
2015-08-01T00:00:00ZCost savings from relaxation of operational constraints on a power system with high wind penetrationMcGarrigle, Edward V.Leahy, Paul G.http://hdl.handle.net/10468/19462015-08-25T13:19:27Z2015-07-01T00:00:00ZCost savings from relaxation of operational constraints on a power system with high wind penetration
McGarrigle, Edward V.; Leahy, Paul G.
Wind energy is predominantly a nonsynchronous generation source. Large-scale integration of wind generation with existing electricity systems, therefore, presents challenges in maintaining system frequency stability and local voltage stability. Transmission system operators have implemented system operational constraints (SOCs) in order to maintain stability with high wind generation, but imposition of these constraints results in higher operating costs. A mixed integer programming tool was used to simulate generator dispatch in order to assess the impact of various SOCs on generation costs. Interleaved day-ahead scheduling and real-time dispatch models were developed to allow accurate representation of forced outages and wind forecast errors, and were applied to the proposed Irish power system of 2020 with a wind penetration of 32%. Savings of at least 7.8% in generation costs and reductions in wind curtailment of 50% were identified when the most influential SOCs were relaxed. The results also illustrate the need to relax local SOCs together with the system-wide nonsynchronous penetration limit SOC, as savings from increasing the nonsynchronous limit beyond 70% were restricted without relaxation of local SOCs. The methodology and results allow for quantification of the costs of SOCs, allowing the optimal upgrade path for generation and transmission infrastructure to be determined.
2015-07-01T00:00:00ZPower system parameters forecasting using Hilbert-Huang transform and machine learningKurbatsky, Victor G.Spiryaev, Vadim A.Tomin, Nikita V.Leahy, Paul G.Sidorov, Denis N.Zhukov, Alexei V.http://hdl.handle.net/10468/17912015-05-07T11:39:12Z2014-04-01T00:00:00ZPower system parameters forecasting using Hilbert-Huang transform and machine learning
Kurbatsky, Victor G.; Spiryaev, Vadim A.; Tomin, Nikita V.; Leahy, Paul G.; Sidorov, Denis N.; Zhukov, Alexei V.
A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.
2014-04-01T00:00:00ZA powerful visualization technique for electricity supply and demand at industrial sites with combined heat and power and wind generationHanrahan, Brian LeifLightbody, GordonStaudt, LawrenceLeahy, Paul G.http://hdl.handle.net/10468/14592014-03-13T03:00:18Z2014-03-01T00:00:00ZA powerful visualization technique for electricity supply and demand at industrial sites with combined heat and power and wind generation
Hanrahan, Brian Leif; Lightbody, Gordon; Staudt, Lawrence; Leahy, Paul G.
The combination of wind generation and combined heat and power (CHP) on an industrial site brings significant design and operational challenges. The stochastic nature of wind power affects the flows of electricity imported and exported to and from the site. Economies of scale favor larger wind turbines, but at the same time it is also desirable to minimize the amount of electricity exported from the site to avoid incurring increased network infrastructure usage charges. Therefore the optimum situation is to maximize the proportion of the site load served by on-site generation. This paper looks at a visualization technique for power flows on an industrial site, which can be used to size on-site generators. The technique is applied to a test case, demonstrating how a simple combined heat and power control scheme can support the integration of on-site wind power. The addition of such CHP control has a small impact on the CHP unit but can greatly increase the proportion of wind generation consumed on-site. This visualization technique allows the comparison of different generation mixes and control schemes in order to arrive at the optimal mix from a technical and economic viewpoint.
2014-03-01T00:00:00Z