Energy Management and Economic Operation Optimization of Microgrid under Uncertainty

Microgrid provides an effective means to promote renewable energy utilization via deploying multiple distributed generations (DGs) with energy storage systems (ESSs), loads, control devices and protect devices, which can operate in either islanded mode or grid-connected mode. In order to coordinate the output of different DGs and realize the potential of renewable energy, energy management and economic dispatch of microgrid is needed. Both distributed energy resources (DERs) and user loads in microgrid have uncertainty characteristics; so the randomness of the wind speed and solar radiation intensity are modeled by interval mathematics and the interval output of the wind turbine and photovoltaic (PV) generation system are obtained. Then, a microgrid economic optimization model based on interval optimization method is proposed. Next, combined with the time-of-use characteristic, issue of the power exchange with the external grid has been considered. Finally, Considering the effect of ESS, this chapter discusses the impacts of uncertainty of renewable energy power and load power on optimization results, as well as the effects of the degree of load uncertainty or load fluctuation on scheduling results. The results verify the robustness and effectiveness of the proposed method in dealing with uncertainty optimization problem of microgrid

A critical evaluation of deterministic methods in size optimisation of reliable and cost effective standalone Hybrid renewable energy systems

Reliability of a hybrid renewable energy system (HRES) strongly depends on various uncertainties affecting the amount of power produced by the system. In the design of systems subject to uncertainties, both deterministic and nondeterministic design approaches can be adopted. In a deterministic design approach, the designer considers the presence of uncertainties and incorporates them indirectly into the design by applying safety factors. It is assumed that, by employing suitable safety factors and considering worst-case-scenarios, reliable systems can be designed. In fact, the multi-objective optimisation problem with two objectives of reliability and cost is reduced to a single-objective optimisation problem with the objective of cost only. In this paper the competence of deterministic design methods in size optimisation of reliable standalone wind-PV-battery, wind-PV-diesel and wind-PV-battery-diesel configurations is examined. For each configuration, first, using different values of safety factors, the optimal size of the system components which minimises the system cost is found deterministically. Then, for each case, using a Monte Carlo simulation, the effect of safety factors on the reliability and the cost are investigated. In performing reliability analysis, several reliability measures, namely, unmet load, blackout durations (total, maximum and average) and mean time between failures are considered. It is shown that the traditional methods of considering the effect of uncertainties in deterministic designs such as design for an autonomy period and employing safety factors have either little or unpredictable impact on the actual reliability of the designed wind-PV-battery configuration. In the case of wind-PV-diesel and wind-PV-battery-diesel configurations it is shown that, while using a high-enough margin of safety in sizing diesel generator leads to reliable systems, the optimum value for this margin of safety leading to a cost-effective system cannot be quantified without employing probabilistic methods of analysis. It is also shown that deterministic cost analysis yields inaccurate results for all of the investigated configurations

Collinsville solar thermal project: energy economics and dispatch forecasting (final report)

The primary aim of this report is to help negotiate a Power Purchase Agreement (PPA) for the proposed hybrid gas-Linear Frensel Reflector (LFR) plant at Collinsville, Queensland, Australia.  The report’s wider appeal is the discussion of the current situation in Australian National Electricity Market (NEM) and techniques and methods used to model the NEM’s demand and wholesale spot prices for the lifetime of the proposed plant. Executive Summary 1        Introduction This report primarily aims to provide both dispatch and wholesale spot price forecasts for the proposed hybrid gas-solar thermal plant at Collinsville, Queensland, Australia for its lifetime 2017-47.  These forecasts are to facilitate Power Purchase Agreement (PPA) negotiations and to evaluate the proposed dispatch profile in Table 3.  The solar thermal component of the plant uses Linear Fresnel Reflector (LFR) technology.  The proposed profile maintains a 30 MW dispatch during the weekdays by topping up the yield from the LFR by dispatch from the gas generator and imitates a baseload function currently provided by coal generators.  This report is the second of two reports and uses the findings of our first report on yield forecasting (Bell, Wild & Foster 2014b). 2        Literature review The literature review discusses demand and supply forecasts, which we use to forecast wholesale spot prices with the Australian National Electricity Market (ANEM) model. The review introduces the concept of gross demand to supplement the Australian Electricity Market Operator’s (AEMO) “total demand”.  This gross demand concept helps to explain the permanent transformation of the demand in the National Electricity Market (NEM) region and the recent demand over forecasting by the AEMO.  We also discuss factors causing the permanent transformation.  The review also discusses the implications of the irregular ENSO cycle for demand and its role in over forecasting demand. Forecasting supply requires assimilating the information in the Electricity Statement of Opportunities (ESO) (AEMO 2013a, 2014c).  AEMO expects a reserve surplus across the NEM beyond 2023-24.  Compounding this reserve surplus, there is a continuing decline in manufacturing, which is freeing up supply capacity elsewhere in the NEM.  The combined effect of export LNG prices and declining total demand are hampering decisions to transform proposed gas generation investment into actual investment and hampering the role for gas as a bridging technology in the NEM.  The review also estimates expected lower and upper bounds for domestic gas prices to determine the sensitivity of the NEM’s wholesale spot prices and plant’s revenue to gas prices. The largest proposed investment in the NEM is from wind generation but the low demand to wind speed correlation induces wholesale spot price volatility.  However, McKinsey Global Institute (MGI 2014) and Norris et al. (2014a) expect economically viable energy storage shortly beyond the planning horizon of the ESO in 2023-24.  We expect that this viability will not only defer investment in generation and transmission but also accelerate the growth in off-market produced and consumed electricity within the NEM region. 2.1     Research questions The report has the following overarching research questions: What is the expected dispatch of the proposed plant’s gas component given the plant’s dispatch profile and expected LFR yield? What are the wholesale spots prices on the NEM given the plant’s dispatch profile? The literature review refines the latter research question into five more specific research questions ready for the methodology: What are the half-hourly wholesale spots prices for the plant’s lifetime without gas as a bridging technology? Assuming a reference gas price of between $5.27/GJ to$7.19/GJ for base-load gas generation (depending upon nodal location;) and for peak-load gas generation of between $6.59/GJ to$8.99/GJ; and given the plant’s dispatch profile What are the half-hourly wholesale spots prices for the plant’s lifetime with gas as a bridging technology? Assuming some replacement of coal with gas generation How sensitive are wholesale spot prices to higher gas prices? Assuming high gas prices are between $7.79/GJ to$9.71/GJ for base-load gas generation (depending upon nodal location); and for peak-load gas generation of between $9.74/GJ to$12.14/GJ; and What is the plant’s revenue for the reference gas prices? How sensitive is the plant’s revenue to gas as a bridging technology? How sensitive is the plant’s revenue to the higher gas prices? What is the levelised cost of energy for the proposed plant? 3        Methodology In the methodology section, we discuss the following items: dispatch forecasting for the proposed plant; supply capacity for the years 2014-47 for the NEM; demand forecasting using a Typical Meteorological Year (TMY); and wholesale spot prices calculation using ANEM, supply capacity and total demand define three scenarios to address the research questions: reference gas prices; gas as a bridging technology; and high gas prices. The TMY demand matches the solar thermal plant’s TMY yield forecast that we developed in our previous report (Bell, Wild & Foster 2014b).  Together, these forecasts help address the research questions. 4        Results In the results section we will present the findings for each research question, including the TMY yield for the LFR and the dispatch of the gas generator given the proposed dispatch profile in Table 3; Average annual wholesale spot prices from 2017 to 2047 for the plant’s node for: Reference gas prices scenario from $18/MWh to$38/MWh Gas as a bridging technology scenario from $18/MWh to$110/MWh High gas price scenario from $20/MWh to$41/MWh The combined plants revenue without subsidy given the proposed profile: Reference gas price scenario $36 million Gas as a bridging technology scenario$52 million High gas price scenario $47 million 5        Discussion In the discussion section, we analyse: reasons for the changes in the average annual spot prices for the three scenarios; and the frequency that the half-hourly spot price exceeds the Short Run Marginal Cost (SRMC) of the gas generator for the three scenarios for: day of the week; month of the year; and time of the day. If the wholesale spot price exceeds the SRMC, dispatch from the gas plant contributes towards profits.  Otherwise, the dispatch contributes towards a loss.  We find that for both reference and high gas price scenarios the proposed profile in Table 3 captures exceedances for the day of the week and the time of the day but causes the plant to run at a loss for several months of the year.  Figure 14 shows that the proposed profile captures the exceedance by hour of the day and Figure 16 shows that only operating the gas component Monday to Friday is well justified.  However, Figure 15 shows that operating the gas plant in April, May, September and October is contributing toward a loss.  Months either side of these four months have a marginal number of exceedances.  In the unlikely case of gas as a bridging scenario, extending the proposed profile to include the weekend and operating from 6 am to midnight would contribute to profits. We offer an alternative strategy to the proposed profile because the proposed profile in the most likely scenarios proves loss making when considering the gas component’s operation throughout the year.  The gas-LFR plant imitating the based-load role of a coal generator takes advantage of the strengths of the gas and LFR component, that is, the flexibility of gas to compensate for the LFR’s intermittency, and utilising the LFR’s low SRMC.  However, the high SRMC of the gas component in a baseload role loses the flexibility to respond to market conditions and contributes to loss instead of profit and to CO2 production during periods of low demand. The alternative profile retains the advantages of the proposed profile but allows the gas component freedom to exploit market conditions.  Figure 17 introduces the perfect day’s yield profile calculated from the maximum hourly yield from the years 2007-13.  The gas generator tops up the actual LFR yield to the perfect day’s yield profile to cover LFR intermittency.  The residual capacity of the gas generator is free to meet demand when spot market prices exceed SRMC and price spikes during Value-of-Lost-Load (VOLL) events.  The flexibility of the gas component may prove more advantageous as the penetration of intermittent renewable energy increases. 6        Conclusion We find that the proposed plant is a useful addition to the NEM but the proposed profile is unsuitable because the gas component is loss making for four months of the year and producing CO2 during periods of low demand.  We recommend further research using the alternative perfect day’s yield profile. 7        Further Research We discuss further research compiled from recommendation elsewhere in the report. 8        Appendix A Australian National Electricity Market Model Network This appendix provides diagrams of the generation and load serving entity nodes and the transmission lines that the ANEM model uses.  There are 52 nodes and 68 transmission lines, which make the ANEM model realistic.  In comparison, many other models of the NEM are highly aggregated. 9        Appendix B Australian National Electricity Market Model This appendix describes the ANEM model in detail and provides additional information on the assumptions made about the change in the generation fleet in the NEM during the lifetime of the proposed plant

Multi-objective design optimisation of standalone hybrid wind-PV-diesel systems under uncertainties

Optimal design of a standalone wind-PV-diesel hybrid system is a multi-objective optimisation problem with conflicting objectives of cost and reliability. Uncertainties in renewable resources, demand load and power modelling make deterministic methods of multi-objective optimisation fall short in optimal design of standalone hybrid renewable energy systems (HRES). Firstly, deterministic methods of analysis, even in the absence of uncertainties in cost modelling, do not predict the levelised cost of energy accurately. Secondly, since these methods ignore the random variations in parameters, they cannot be used to quantify the second objective, reliability of the system in supplying power. It is shown that for a given site and uncertainties profile, there exist an optimum margin of safety, applicable to the peak load, which can be used to size the diesel generator towards designing a cost-effective and reliable system. However, this optimum value is problem dependent and cannot be obtained deterministically. For two design scenarios, namely, finding the most reliable system subject to a constraint on the cost and finding the most cost-effective system subject to constraints on reliability measures, two algorithms are proposed to find the optimum margin of safety. The robustness of the proposed design methodology is shown through carrying out two design case studies

Intelligent power system operation in an uncertain environment

This dissertation presents some challenging problems in power system operations. The efficacy of a heuristic method, namely, modified discrete particle swarm optimization (MDPSO) algorithm is illustrated and compared with other methods by solving the reliability based generator maintenance scheduling (GMS) optimization problem of a practical hydrothermal power system. The concept of multiple swarms is incorporated into the MDPSO algorithm to form a robust multiple swarms-modified particle swarm optimization (MS-MDPSO) algorithm and applied to solving the GMS problem on two power systems. Heuristic methods are proposed to circumvent the problems of imposed non-smooth assumptions common with the classical approaches in solving the challenging dynamic economic dispatch problem. The multi-objective combined economic and emission dispatch (MO-CEED) optimization problem for a wind-hydrothermal power system is formulated and solved in this dissertation. This MO-CEED problem formulation becomes a challenging problem because of the presence of uncertainty in wind power. A family of distributed optimal Pareto fronts for the MO-CEED problem has been generated for different scenarios of capacity credit of wind power. A real-time (RT) network stability index is formulated for determining a power system\u27s ability to continue to provide service (electric energy) in a RT manner in case of an unforeseen catastrophic contingency. Cascading stages of fuzzy inference system is applied to combine non real-time (NRT) and RT power system assessments. NRT analysis involves eigenvalue and transient energy analysis. RT analysis involves angle, voltage and frequency stability indices. RT Network status index is implemented in real-time on a practical power system --Abstract, page iv

Advanced Studies on Locational Marginal Pricing

The effectiveness and economic aspect of Locational Marginal Price (LMP) formulation to deal with the power trading in both Day-Ahead (DA) and Real-Time (RT) operation are the focus of not only the system operator but also numerous market participants. In addition, with the ever increasing penetration of renewable energy being integrated into the grid, uncertainty plays a larger role in the process of market operation. The study is carried out in four parts. In the first part, the mathematical programming models, which produce the generation dispatch solution for the Ex Post LMP, are reviewed. The existing approach fails to meet the premise that Ex Post LMP should be equal to Ex Ante LMP when all the generation and load combinations in RT operation remain the same as in DA market. Thus, a similar yet effective approach which is based on a scaling factor applied to the Ex Ante dispatch model is proposed. In the second part, the step change characteristic of LMP and the Critical Load Level (CLL) effect are investigated together with the stochastic wind power to evaluate the impacts on the market price volatility. A lookup table based Monte Carlo simulation has been adopted to capture the probabilistic nature of wind power as well as assessing the probabilistic distribution of the price signals. In the third part, a probability-driven, multilayer framework is proposed for ISOs to schedule intermittent wind power and other renewables. The fundamental idea is to view the intermittent renewable energy as a product with a lower quality than dispatchable power plants, from the operator’s viewpoint. The new concept used to handle the scheduling problem with uncertainty greatly relieves the intensive computational burden of the stochastic Unit Commitment (UC) and Economic Dispatch (ED). In the last part, due to the relatively high but similar R/X ratio along the radial distribution feeder, a modified DC power flow approach can be used to simplify the computational effort. In addition, distribution LMP (DLMP) has been formulated to have both real and reactive power price, under the linearized optimal power flow (OPF) model