Load dispatch optimization of open cycle industrial gas turbine plant incorporating operational, maintenance and environmental parameters

Power generation fuel cost, unit availability and environmental rules and regulations are important parameters in power generation load dispatch optimization. Previous optimization work has not considered the later two in their formulations. The objective of this work is to develop a multi-objective optimization model and optimization algorithm for load dispatching optimization of open cycle gas turbine plant that not only consider operational parameters, but also incorporates maintenance and environmental parameters. Gas turbine performance parameters with reference to ASME PTC 22-1985 were developed and validated against an installed performance monitoring system (PMS9000) and plant performance test report. A gas turbine input-output model and emission were defined mathematically into the optimization multi-objectives function. Maintenance parameters of Equivalent Operating Hours (EOH) constraints and environmental parameters of allowable emission (NOx, CO and SO2) limits constraints were also included. The Extended Priority List and Particle Swarm Optimization (EPL-PSO) method was successfully implemented to solve the model. Four simulation tests were conducted to study and test the develop optimization software. Simulation results successfully demonstrated that multi-objectives total production cost (TPC) objective functions, the proposed EOH constraint, emissions model and constraints algorithm could be incorporated into the EPL-PSO method which provided optimum results, without violating any of the constraints as defined. A cost saving of 0.685% and 0.1157% could be obtained based on simulations conducted on actual plant condition and against benchmark problem respectively. The results of this work can be used for actual plant application and future development work for new gas turbine model or to include additional operational constraint

Decarbonising Future Power Systems by Demand Side Management in Smart Grid

Carbon emission reduction is an urgent global task. Renewable energy sources integration can promote the transformation of cleaner and greener power system. But the time-varying nature of these sources causes indeterminacy problems. Smart grid is a powerful tool that can deal with these problems in electricity aspect. One of the key smart grid technologies is demand side management. How to use demand side management to regulate and decarbonise the power system is the main point of this thesis. In order to integrate renewable energy sources, a day-ahead electricity market scheme is proposed, involving the utility, the demand response aggregator and customers. This model leads to a multiobjective optimization problem, which is solved by an artificial immune algorithm. The simulation results confirm the feasibility and robustness of the proposed model. All participants can benefit from it, and the system power peak to average ratio can be reduced. In order to realize the carbon emission reduction, a system model for annual fuel sources scheduling and operational policy making of electricity generation is established, considering the economic, environmental and social aspects. A minimum Manhattan distance approach is proposed to select the final solution. The impacts of carbon tax and renewable obligation on carbon emission, generation cost and electricity bill are examined. These can reveal the proper strategy for deciding renewable energy source and carbon emission related policies. After that, a carbon emission flow model is introduced to facilitate the analysis and assessment of demand side management’s impacts on carbon emission reduction. The time sensitivity of carbon emission in both generation side and customer side are obtained. The daily case and seasonal case are presented. The simulation results show that the load curtailment and load shift approaches can effectively reduce the carbon emission

Energy Management of Grid-Connected Microgrids, Incorporating Battery Energy Storage and CHP Systems Using Mixed Integer Linear Programming

In this thesis, an energy management system (EMS) is proposed for use with battery energy storage systems (BESS) in solar photovoltaic-based (PV-BESS) grid-connected microgrids and combined heat and power (CHP) applications. As a result, the battery's charge/discharge power is optimised so that the overall cost of energy consumed is minimised, considering the variation in grid tariff, renewable power generation and load demand. The system is modelled as an economic load dispatch optimisation problem over a 24-hour time horizon and solved using mixed integer linear programming (MILP) for the grid-connected Microgrid and the CHP application. However, this formulation requires information about the predicted renewable energy power generation and load demand over the next 24 hours. Therefore, a long short-term memory (LSTM) neural network is proposed to achieve this. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the energy management system (EMS) that benefits from using actual generation and demand data in real-time. At each time-step, the LSTM predicts the generation and load data for the next 24 h. The dispatch problem is then solved, and the real-time battery charging or discharging command for only the first hour is applied. Real data are then used to update the LSTM input, and the process is repeated. Simulation results using the Ushant Island as a case study show that the proposed online optimisation strategy outperforms the offline optimisation strategy (with no RH), reducing the operating cost by 6.12%. The analyses of the impact of different times of use (TOU) and standard tariff in the energy management of grid-connected microgrids as it relates to the charge/discharge cycle of the BESS and the optimal operating cost of the Microgrid using the LSTM-MILP-RH approach is evaluated. Four tariffs UK tariff schemes are considered: (1) Residential TOU tariff (RTOU), (2) Economy seven tariff (E7T), (3) Economy ten tariff (E10T), and (4) Standard tariff (STD). It was found that the RTOU tariff scheme gives the lowest operating cost, followed by the E10T tariff scheme with savings of 63.5% and 55.5%, respectively, compared to the grid-only operation. However, the RTOU and E10 tariff scheme is mainly used for residential applications with the duck curve load demand structure. For community grid-connected microgrid applications except for residential-only communities, the E7T and STD, with 54.2% and 39.9%, respectively, are the most likely options offered by energy suppliers. The use of combined heat and power (CHP) systems has recently increased due to their high combined efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Secondly, if the load drops below a predefined threshold, the engine will operate at a lower temperature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned issues may be solved by combining CHP with a battery energy storage system. However, the dispatch of CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power export to the grid due to prediction errors. Therefore, a real-time EMS using a combination of LSTM neural networks, MILP, and RH control strategy is proposed. Simulation results show that the proposed method can prevent power export to the grid and reduce the operational cost by 8.75% compared to the offline method. The finding shows that the BESS is a valuable asset for sustainable energy transition. However, they must be operated safely to guarantee operational cost reduction and longer life for the BESS

An economic evaluation of the potential for distributed energy in Australia

Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) recently completed a major study investigating the value of distributed energy (DE; collectively demand management, energy efficiency and distributed generation) technologies for reducing greenhouse gas emissions from Australia’s energy sector (CSIRO, 2009). This comprehensive report covered potential economic, environmental, technical, social, policy and regulatory impacts that could result from the wide scale adoption of these technologies. In this paper we highlight the economic findings from the study. Partial Equilibrium modeling of the stationary and transport sectors found that Australia could achieve a present value welfare gain of around $130 billion when operating under a 450 ppm carbon reduction trajectory through to 2050. Modeling also suggests that reduced volatility in the spot market could decrease average prices by up to 12% in 2030 and 65% in 2050 by using local resources to better cater for an evolving supply-demand imbalance. Further modeling suggests that even a small amount of distributed generation located within a distribution network has the potential to significantly alter electricity prices by changing the merit order of dispatch in an electricity spot market. Changes to the dispatch relative to a base case can have both positive and negative effects on network losses.Distributed energy; Economic modeling; Carbon price; Electricity markets

Multi-objective based economic environmental dispatch with stochastic solar-wind-thermal power system

This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system

Allocation, incentives and distortions: the impact of EU ETS emissions allowance allocations to the electricity sector

The allowance allocation under the European Emission trading schemes differs fundamentally from earlier cap and trade programs, like SO2 and NOx in the USA. Because of the iterative nature of negotiations of the overall budget, the allocation also has to follow an iterative process. If power generators anticipate that their current behaviour will affect future allowance allocation, then this can distort today’s decisions. Furthermore, the National Allocation Plans (NAPs) contain multiple provisions dealing with existing installations, what happens to allocation when they close, and allocations to new entrants. We provide a framework to assess the economic incentives and distortions that provisions in NAPs can have on market prices, operation and investment decisions. To this end, we use both analytic models to illustrate the incentives effects and results from numerical simulation runs that estimate the magnitude of impacts from different allocation rules