Miguel A. Ortega-Vazquez

Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration

Spinning reserve (SR) allows system operators to compensate for unpredictable imbalances between load and generation caused by sudden outages of generating units, errors in load forecasting or unexpected deviations by generating units from their production schedules. As the proportion of power produced by wind farms increases, it becomes more difficult to predict accurately the total amount of power injected by all generators into the power system. This added uncertainty must be taken into account when setting the requirement for SR. This paper proposes a technique to calculate the optimal amount of SR that the system operator should provide to be able to respond not only to generation outages but also to errors in the forecasts for load and wind power production. Using a Monte Carlo simulation, the proposed technique for setting the SR requirements is then compared with the traditional deterministic criterion (i.e., the capacity of the largest online infeed), an approach to cope with wind imbalances and an approach that combines the traditional criterion with the approach to cope with wind imbalances. The results show that, contrary to what is commonly believed, an increased wind power penetration does not necessarily require larger amounts of SR.

Optimizing the Spinning Reserve Requirements Using a Cost/Benefit Analysis

Spinning reserve (SR) is one of the most important resources used by system operators to respond to unforeseen events such as generation outages and sudden load changes. While keeping large amounts of generation in reserve protects the power system against the generation deficits that might arise from different contingencies, and thus reduces the probability of having to resort to load shedding, this reserve provision is costly. Traditional unit commitment (UC) formulations use deterministic criteria, such as the capacity of the largest online generator to set the SR requirements. Other UC formulations adjust this requirement based on probabilistic criteria but require iterative processes or approximate calculations of the level of risk associated with the provision of reserve. This paper describes an offline method for setting the SR requirements based on the cost of its provision and the benefit derived from its availability

Electric Vehicle Aggregator/System Operator Coordination for Charging Scheduling and Services Procurement

Summary form only given. In response to the need for the decarbonization of the transport sector, it is expected that large fleets of electric vehicles (EVs) will constitute an important share of the electricity demand. This evolution is likely to be accompanied by a parallel evolution of the electricity supply business with the deployment of smart grid technologies. As a consequence, it is expected that demand will feature higher potential for communication and control, which will enable its active participation in the daily operational planning of power systems. In particular, EVs being equipped with a battery can both defer their demand or inject electricity back into the system. However, to achieve volumes that can have an impact on the system, these demands need to be aggregated and operated as an ensemble. This paper proposes the necessary adaptations to include the input of EV aggregation to electricity markets. This permits the scheduling of EV charging and services in coordination with the system operator thus enhancing the power systems efficiency and security while reducing its environmental impact. Results show that the EVs penetration levels that the system would be able to absorb without requiring expansion of the supply side, are significantly increased when coordination over their charging schedule is performed.

Assessing the Impact of Wind Power Generation on Operating Costs

Wind power generation is taking an increasing share of the overall energy production in many power systems. While its low marginal operating cost reduces the overall cost of meeting the demand for electrical energy, the stochastic and intermittent nature of wind generation increases the uncertainty that the system operators face and obliges them to procure additional reserve capacity. This paper presents a methodology for quantifying fully the effect of wind power generation on the various components of the cost of operating the system.

A Hybrid Stochastic/Interval Approach to Transmission-Constrained Unit Commitment

This paper proposes a new transmission-constrained unit commitment method that combines the cost-efficient but computationally demanding stochastic optimization and the expensive but tractable interval optimization techniques to manage uncertainty on the expected net load. The proposed hybrid unit commitment approach applies the stochastic formulation to the initial operating hours of the optimization horizon, during which the wind forecasts are more accurate, and then switches to the interval formulation for the remaining hours. The switching time is optimized to balance the cost of unhedged uncertainty from the stochastic unit commitment against the cost of the security premium of the interval unit commitment formulation. These hybrid, stochastic, and interval formulations are compared using Monte Carlo simulations on a modified 24-bus IEEE Reliability Test System. The results demonstrate that the proposed unit commitment formulation results in the least expensive day-ahead schedule among all formulations and can be solved in the same amount of time as a full stochastic unit commitment. However, if the range of the switching time is reduced, the hybrid formulation in the parallel computing implementation outperforms the stochastic formulation in terms of computing time.

Optimal Operation and Services Scheduling for an Electric Vehicle Battery Swapping Station

For a successful rollout of electric vehicles (EVs), it is required to establish an adequate charging infrastructure. The adequate access to such infrastructure would help to mitigate concerns associated with limited EV range and long charging times. Battery swapping stations are poised as effective means of eliminating the long waiting times associated with charging the EV batteries. These stations are mediators between the power system and their customers. In order to successfully deploy this type of stations, a business and operating model is required, that will allow it to generate profits while offering a fast and reliable alternative to charging batteries. This paper proposes an optimization framework for the operating model of battery swapping stations. The proposed model considers the day-ahead scheduling process. Battery demand uncertainty is modeled using inventory robust optimization, while multi-band robust optimization is employed to model electricity price uncertainty. The results show the viability of the proposed model as a business case, as well as the effectiveness of the model to provide the required service.

The value of operational flexibility in power systems with significant wind power generation

There is a growing body of evidence demonstrating how large penetrations of wind power generation in power systems contribute to increase the cost and the complexity of grid operations. Those costs and increased complexity are directly linked to the random nature of the wind over time, which requires system operators to carry more reserve capacity to cope with that randomness if current security and reliability standards are to be maintained. Moreover, as the frequency spectrum of the wind generation random process is relatively wide (from 10−6 to about slightly above 1 Hz), the reserves available must be capable to be deployed fast enough to counter this variability. Therefore, in systems with significant wind power penetrations the security-constrained unit commitment programs should be capable of capturing the reserve capacity deployment requirements entailed by the random wind dynamics. More fundamentally, however, what is required is that the dispatchable portion of the generation system providing reserves is flexible enough. In other words, there must be enough flexible capacity available to ramp up and down so to shadow the winds caprices. In this paper, we formulate a modification of the classic unit commitment formulation ot assess the value of such operational flexibility in power systems with large proportions of wind capacity. We discuss some economic and technical indicators of flexibility.

Optimal Participation of an Electric Vehicle Aggregator in Day-Ahead Energy and Reserve Markets

An aggregator acts as a mediator between the system operator and residential customers, enabling mutually beneficial coordination for electric vehicle (EV) owners and the power system. The aggregator aims to maximize its profits from trading energy and regulation reserve in wholesale markets. Since the aggregator does not own EV batteries, the EV owners must be reimbursed for the degradation of their batteries due to the additional cycling beyond transportation needs. This paper proposes a bidding strategy for the aggregator to maximize its profits from participating in competitive energy and different regulating reserves markets, while compensating EV owners for degradation. The results show that depending on the battery cost, the aggregator splits its resources between the energy and reserves markets. The results also show the system operator attains cost savings, if an aggregator uses EVs to provide services.

Economic impact assessment of load forecast errors considering the cost of interruptions

Load forecast (LF) is the prediction of the system load over a defined time interval that ranges from one hour to one week for power system operation purposes. Due to the random nature of the load and its dependency on numerous factors forecasting is never 100% accurate. Inaccuracies in the LF lead to inefficient daily system operation. On the one hand, if at a given period the load is under-forecasted the available capacity might not be enough to meet the demand and the spinning reserve (SR) requirements. This will result in a large expectation of energy not supplied in case of generating unit outages. On the other hand if at a given period the load is over-forecasted the schedule might consider unnecessary start-ups and the provision of excessive SR. This paper explores the economical impact of the LF errors on the daily power system operation. In this paper the probability of generating units outages is considered in order to estimate the energy not served due outages. The daily operating cost of the system is divided into three terms, the start-up cost, the dispatch cost and the cost of expected energy not served due to outages of generating units

Should the spinning reserve procurement in systems with wind power generation be deterministic or probabilistic

It is a common practice by the system operators to set the spinning reserve (SR) provision based on deterministic approaches. But is this the best operating policy? This paper presents a comparison between the traditional N−1 criterion and two probabilistic approaches to estimate the optimal amount of SR requirements. This comparison shows that methods based on a cost-benefit analysis achieve a better economical balance. An approach based on an exogenous cost-benefit analysis capable of handling systems of realistic size is then presented. This approach is then extended to include demand and wind power generation forecast errors.