Non-dispatchable Technologies Research Articles

Quantifying resilience in energy systems with out-of-sample testing

The need to design resilient energy systems becomes ever more apparent as we face the challenge of decarbonising through reliance on non-dispatchable technologies and sectoral integration. Increasingly, modelling efforts focus on improving system resilience, but fail to quantify the improvements. In this paper, we propose a novel workflow that allows increases in resilience to be measured quantitatively. It incorporates out-of-sample testing following optimisation, and compares the impacts of demand and power interruption uncertainty on both risk-unaware and risk-aware district energy system models. To ensure we encompass the full range of impacts caused by uncertainty, we consider nine distinct objectives encompassing differences in: investment and operation costs, CO2 emissions, and aversion to risk.We apply the workflow in a case study in Bangalore, India, and demonstrate that scenario optimisation improves system resilience by one to two orders of magnitude. However, systems designed for resilience to demand uncertainty are not able to gracefully extend to managing risk from extreme shocks to the system, such as power interruptions. We show that shock-induced instability can be addressed by specific measures to reduce grid dependence. Finally, by studying out-of-sample test results, we identify an objective which balances cost, CO2 emissions, and system resilience; this balance is achieved by novel application of the Conditional Value at Risk measure. These results expose the need for out-of-sample testing whenever uncertainty is considered in energy system modelling, and we provide the framework with which it can be undertaken.

On flexible hydropower and security of supply: Spain beyond 2020

Generation adequacy is a key ingredient to security of electricity supply (SoS). Some national plans envisage a future decrease in the number of coal-fired stations and an increase in renewable installed capacity. This forecast, along with the future reduction of nuclear capacity, will lead to a combination of less baseload plants and sizeable intermittent generation. Hence there is a risk that supply will be unable to meet demand and generation adequacy will suffer.We assess how the flexible management of hydro resources can alleviate this risk by adjusting power generation to peak demand. Indeed there is empirical evidence that they are positively correlated. We compute this correlation in the case of Spain (an ‘electric island’). Besides, hydro plants operate in combination with other non-dispatchable technologies within the system. Therefore, we also take their hourly seasonality into account. Next we run a Monte Carlo simulation to derive the risk profile of several adequacy metrics in the coming decades. Our results show that flexible hydro generation certainly mitigates the risk but is insufficient to bring an adecuate level of SoS when the enhanced renewable capacity goes hand in hand with a decreased baseload capacity. The risk further decreases after accounting for seasonal non-dispatchable generation, yet it still looms large. These results can be important for policy makers, system operators, and power companies when analizing investments in renewable energy with a long lifespan.

Peak-load pricing with different types of dispatchability

We extend the theory of peak-load pricing by considering that the production with different technologies can be adjusted within their capacity at different speeds. In the established analysis, all production decisions can be made after the random variables realize. In our setting, in contrast, some decisions are made before, others after. This is important, e.g., when increasing capacities of renewables are integrated in electricity systems worldwide. We consider fixed load and three types of capacities: partially dispatchable capacity (e.g., nuclear power-plants) needs to be scheduled ahead of actual production, non-dispatchable capacity (e.g., wind turbines) produces randomly, and highly-dispatchable capacity (e.g., gas turbines) can instantly adjust. If capacities differ in their dispatchability, some standard results of peak-load pricing break down. For example, less capacity types will be employed. Either a system with partially dispatchable technologies only, or a system dominated by non-dispatchable technologies and supplemented by highly-dispatchables occurs. Non- and highly dispatchable technologies can be substitutes or complements. The probability of outage does not rise if non-dispatchable capacity becomes cheaper. In a system with non-dispatchables, capacity decisions cannot be decentralized by conventional markets because cost recovery is not possible. Thus, the integration of renewable electricity generators requires alternative market designs.

Peak-Load Pricing with Different Types of Dispatchability

We extend the theory of peak-load pricing by considering that the production with different technologies can be adjusted within their capacity at different speeds. In the established analysis, all production decisions can be made after the random variables realize. In our setting, in contrast, some decisions are made before, others after. We consider fixed load and three types of capacities: partially dispatchable capacity needs to be scheduled ahead of actual production, non-dispatchable capacity produces randomly, and highly-dispatchable capacity can instantly adjust. If capacities differ in their dispatchability, some standard results of peak-load pricing break down, e.g., less capacity types will be employed. Either a system with partially dispatchable technologies only, or a system dominated by non-dispatchable technologies and supplemented by highly-dispatchables occurs, where non- and highly dispatchable technologies could be substitutes or complements. For the latter system capacity decisions cannot be decentralized by markets because costs recovery is not possible.

On optimal participation in the electricity markets of wind power plants with battery energy storage systems

The recent cost reduction and technological advances in medium- to large-scale battery energy storage systems (BESS) makes these devices a true alternative for wind producers operating in electricity markets. Associating a wind power farm with a BESS (the so-called virtual power plant (VPP)) provides utilities with a tool that converts uncertain wind power production into a dispatchable technology that can operate not only in spot and adjustment markets (day-ahead and intraday markets) but also in ancillary services markets that, up to now, were forbidden to non-dispatchable technologies. What is more, recent studies have shown capital cost investment in BESS can be recovered only by means of such a VPP participating in the ancillary services markets. We present in this study a multi-stage stochastic programming model to find the optimal operation of a VPP in the day-ahead, intraday and secondary reserve markets while taking into account uncertainty in wind power generation and clearing prices (day-ahead, secondary reserve, intraday markets and system imbalances). A case study with real data from the Iberian electricity market is presented.

Informing decision makers and identifying niche opportunities for windpower: Use of multiattribute trade off analysis to evaluate non-dispatchable resources

The operational and flexibility characteristics of renewable energy technologies are often overlooked in traditional head to head technology comparisons. This impedes their adoption since identification of environmental and risk mitigation advantages requires evaluation of such non-dispatchable technologies in a systemwide context. Use of multiattribute resource planning tools in a trade off analysis framework identifies the complementary emissions reduction and fuel diversification characteristics of renewables. Data visualization using trade off analysis communicates electric resource interactions and the risks of following various strategies to diverse stakeholder audiences, promoting acceptance. This paper provides an overview of the multiattribute trade off approach and applies it to resource strategies incorporating windpower in the New England regional power system. Examples focus on the interaction of wind resources with demand-side management and supply-side options under fuel cost uncertainty.

A Two-Phase Decomposition Approach for Electric Utility Capacity Expansion Planning Including Nondispatchable Technologies

This paper presents a model and a two-phase algorithm for incorporating nondispatchable technologies or energy sources as decision variables in long-range electric utility capacity expansion plans. The first phase uses a deterministic model to quickly obtain a near-optimal expansion plan. This model employs a step approximation to the time-dependent availabilities of the nondispatchable technologies, and a derating technique to represent forced outages of conventional units. Specialized algorithms that exploit the structure of this deterministic problem are developed. The second phase refines the resulting solution toward the probabilistic optimum. It employs accurate probabilistic production costing techniques to account for forced outages of conventional generators, and uses hour-by-hour simulation to represent the contributions of nondispatchable technologies. This second phase requires few additional iterations in our computational experience. The proposed approach suggests a modification for the existing state-of-the-art methodology electric generation expansion analysis system (EGEAS), particularly for situations in which the cumulant approximation used in EGEAS may be inadequate, and it suggests an improved technique for accelerating Bloom's Generalized Benders' Decomposition algorithm for the conventional equipment capacity expansion planning problem.

The Introduction of NonDIispatchable Technologies a Decision Variables in Long-Term Generation Expansion Models

Non-dispatchable technologies (solar, wind, run-of-the-ri ver hydro, cogeneration) affect the cost of electricity production in a complex manner by modifying the probability distribution of demand for conventional generation. The lack of an appropriate methodology to efficiently derive this modification has prevented inclusion of non-dispatchable technologies as decision variables in capacity expansion models. This paper develops a stochastic approach to load modification which explicitly models two types of interdependencies between load and non-dispatchable generation: through time of day and through weather. If more than one non-dispatchable generation technology is considered, the dependency among them is also modeled. Furthermore, the total as well as the marginal impact of non-dispatchable capacity on system reliability and operating cost is derived. This allows us to model non-dispatchable generation in the context of two broad classes of optimization algorithms. Dynamic programming and mathematical decomposition are considered in this paper as characteristic examples of algorithms in each class. Load modification models already in use derive total cost impact only, and are based on hourly chronological simulation which is a computationally cumbersome method. The methodology developed here provides marginal impact in addition to total impact values, is computationally efficient and is applicable to future demand projections at almost any level of detail. Finally, its accuracy proved very satisfactory when tested on 1975 Miami load and insolation data.

The Introduction of Non-Dispatchable Technologies as Decision Variables in Long-Term Generation Expansion Models

The Introduction of Non-Dispatchable Technologies as Decision Variables in Long-Term Generation Expansion Models