Assigning Operating Reserves in Power Systems Under Imminent Intelligent Attack Threat

— In a restructured power system, Ancillary services (AS) are required to balance load generation mismatches and to meet unforeseen contingencies. Operating Reserve is a major part of AS which is highly uncertain to forecast, mainly due to unpredictability of customer needs, over or under production of Energy and unpredictability in the integration of renewable energy sources. In this work wind integration is considered as a factor to forecast operating reserve. Increase of wind integration into power system, needs larger quantities of operating reserve. This demands an increase in the cost of generation and emissions. Forecasting the Operating Reserve Ancillary Service helps the system operators (SO) to plan scheduling of generators in advance and also in better bidding environment. Forecasting tools like feed-forward networks, Time series models were used to forecast load and Electricity price in the past. In this paper a hybrid method consisting of Support Vector Machines (SVM) and Fuzzy Interface System (FIS) is used to forecast Operating Reserve in Day-ahead market. Case studies using CAISO and ERCOT ISOs are presented. The SVM-FIS method is found to be better forecasting tool to predict the operating reserve Ancillary Service.

Electric power systems (EPS) are greatly impacted by renewable energy sources (RES) in modern days due to the environmental advantages that they bring when compared to conventional generation technologies, but it is well known that they also bring challenges to the operation and planning of the EPS due to the inherent uncertainties of weather conditions, low inertia, among others. This negatively impacts the reliability of the EPS and the effects of such uncertainties increase as the level of RES penetration rises. This problem is addressed by operators and planners with the operating reserves, that need to ensure the reliability of the EPS. This work presents a review of the current approaches found in the literature that address the problem of operating reserves in the context of operation and planning.

The growing share of electricity generation from renewable energy creates difficulties in maintaining the balance of generation and demand. This is mainly due to uncertainties caused by prediction errors in renewable generation. In order to maintain power system security, the participation of demand side response to the balancing services such as operating reserve is critical. In this paper, the capability of smart appliances to act as operating reserves for the system operator is investigated. The smart appliances considered are washing machines, dish washers and tumble dryers equipped with communication modules. A novel framework is introduced which enables system operators to access demand response from smart appliances in a timeframe suitable for operating reserves. A mathematical model is developed to simulate appliances with multiple discrete power phases. The delay and interruption of appliances cycles are considered in the model. A multiple time-step simulation is introduced that assesses the load reduction from a number of households as a response to a reserve instruction which is modelled as a price increase with a short notification period. The results are used to estimate the available demand response from Great Britain (GB) households at any moment of the day. With a 20% penetration of smart appliances, the demand response can provide up to 54% of the operating reserve requirements of the GB power system depending on the time of day.

The growing integration of renewable energy in power systems demands for adequate planning of generation systems not only to meet long-term capacity requirements hut also to cope with sudden capacity shortages that can occur during system operation. As a matter of fact, system operators must schedule an adequate amount of operational reserve to avoid capacity deficits which can be caused by, for instance, overestimating the wind power that will be available. The framework proposed for the long-term assessment of operational reserve relies on the Nave forecasting method to produce wind power forecasts for the next hour. This forecasting model is simple and widely used to obtain short-term forecasts. However, it has been shown that regression models, such as the Autoregressive Integrated Moving Average (ARIMA) model, can outperform the Naive model even for forecasting horizons of up to 1 hour. This paper investigates the differences in the risk indices obtained for the long-term operational reserve when using the Naive and the ARIMA forecasting models. The objective is to assess the impact of the forecasting error in the long-term operational reserve risk indices. Experiments using the Sequential Monte Carlo Simulation (SMCS) method were carried out on a modified version of the IEEE RTS 79 test system that includes wind and hydro power variability. A sensitivity analysis was also performed taking into account several wind power integration scenarios and two different merit orders for scheduling generating units.

Estimating the additional operating reserve in power systems with installed renewable energy sources

Electric power systems are subject to failures, due to both deliberate and fortuitous events. This paper addresses the first case in which a disruptive agent aims at maximizing the damage to the network (expressed through the total cost of operation), while the system operator takes the necessary measures to mitigate the effects of this attack. The interaction between these two agents is modeled by means of a bi-level optimization problem. On one hand, the disruptive agent is positioned in the upper-level optimization problem and must decide which elements to render out of service (lines and generators), given a limited destructive budget. On the other hand, the system operator, located in a lower-level optimization problem, reacts to the attack by deploying mitigation measures in order to minimize cost overruns in system operation. Based on the aforementioned dynamic, this paper proposes a novel approach to maximize the resiliency of the power system under intentional attacks through the implementation of distributed energy resources (DERs), namely, distributed generation (DG) and demand response (DR). Three metrics are proposed to assess resilience by assigning DERs in islands generated by the destruction of lines and generators. The results obtained in a didactic 5-bus test system and the IEEE RTS-24 bus test system demonstrate the applicability and effectiveness of the proposed methodology.

Guest Editorial: Situational awareness of integrated energy systems

The integration of large shares of wind generation in power systems requires the development of new algorithms and forecasting tools for making decisions in the operational domain taking into account wind generation forecast uncertainties. One of these decisions regards operating reserve requirements to meet load and wind variations. The aim of this paper is therefore to address this issue from a risk evaluation perspective, showing that it is possible to describe the consequences of each possible reserve level through a set of risk indices useful for decision-making. The new reserve management tool described in this paper is intended to support the transmission system operator (TSO) in defining on-line the operating reserve needs for the daily and intraday markets. Decision strategies like setting an acceptable risk level or finding a compromise between economic issues and the risk of loss of load are explored. A case-study based on the Portuguese power system demonstrates the usefulness and efficiency of the tool.

Wind power generation has the characteristics of intermittency and uncertainty. It is significant to predict intervals of short-term wind power precisely for optimizing the grid power system operation scheduling and reserve capacity. The paper proposed a simple short-term wind power intervals prediction model based on artificial bee colony-neural network (ABC-NN). A new criterion was developed in this method to get better prediction results. Then generalized morphological filter was applied in the de-noising of wind power time sequence by analyzing of wind power constitute components. The simulation results demonstrate that the new method has a better performance in wind power intervals prediction.

Intelligent attack defense scheme based on DQL algorithm in mobile fog computing

In February 1998 a new and unique form of power generator, the Powerformer TM , was introduced into the marketplace. The Powerformer can be operated at transmission system level voltage without the need for a step up transformer and consequently has unique system support capabilities. The aim of the research work, presented in this thesis, was to investigate the unique aspects of the Powerformer from a power system perspective, in order to facilitate optimal power system operation and security.The main focus of the research undertaken by the author of this thesis was to investigate the impact of Powerformer on the relative voltage stability of a power system. The driving force behind the author's desire to look at the impact of the Powerformer on power system voltage stability and security was the realisation that voltage stability issues are becoming increasingly important. The focus for today's modem power transmission systems is undergoing a radical shift from a purely publicly provided service to a financially driven commodity. This ultimately means that power systems are increasingly under pressure to be utilised to their fullest capacity without the need to install new, expensive and sometimes socially unpopular system equipment and facilities. The problem with this situation is that a system's stability can be affected greatly by loading conditions. When a system is heavily loaded the voltage stability limit, rather than the transient angle stability limit constrains its operation. It is for this reason that voltage stability concerns in particular are increasingly being discussed in the literature and becoming an issue for power system operators. A number of different static and dynamic system analysis techniques have been reviewed and compared in this thesis, with the results of these comparisons presented and discussed in this thesis. Amongst the methods discussed in this thesis, methods for use in voltage stability analysis and assessment have been proposed and presented by the author of this thesis. The most suitable techniques are being utilised to determine how the Powerformer can aid in safe system operation under a number of different system contingencies in a number of different test systems, including the 600+ bus Queensland Transmission System. Particular emphasis has been placed in this research on long-term system dynamics. Two major aspects of the Powerformer that differ it from conventional are the lack of a step up transformer and the ability of the Powerformer stator to be overloaded for longer periods than conventional power generators. The lack of a step up transformer means that power that would normally be lost can be supplied to the system and that the high voltage bus, normally on the opposite side of the step up transformer, is directly controlled. The overload capability of the Powerformer means that it can supply reactive power support to the power system for a longer period than a conventional generator. Investigations undertaken into the Powerformer have shown it to have a potentially beneficial ability to allow greater system loading capability and therefore system security margins and a potentially beneficial impact on the time to voltage collapse following a system contingency.

This paper defines flexibility reserve trajectory as a single reserve product that not only supplies the energy imbalance in real-time operation but also the resulting ramping requirements by embedding the flexible ramping trajectories as time derivative of the reserve trajectories. Further, a stochastic optimization model is proposed for multi-fidelity co-optimization of energy and flexibility reserve in day-ahead power systems operation. The proposed model integrates operation decisions with different levels of modeling fidelity in a single stochastic optimization problem. More specifically, day-ahead energy trajectories, day-ahead flexibility reserve capacity trajectories, and real-time flexibility reserve deployment trajectories are modeled by Bernstein polynomials with different degrees as required to match the load and renewable generation with different levels of variability in day-ahead and real-time operations. The proposed model is implemented on the IEEE reliability test system using load and solar generation data of California ISO, where the numerical results demonstrate that the proposed model schedules a reserve capacity that more accurately matches the real-time energy imbalance and ramping requirements of net-load, while reducing the total operation cost of the system.

In order to improve the reserve adequacy of high proportion new energy power systems and realize the efficient consumption of large-scale new energy, the source of reserve resources is expanded to the source network and load storage, and the operating reserve level that can be provided by the source network and load storage flexible regulating resources is quantified in terms of the direction of regulation and the mode of participation. Then, according to the regulation idea of “synchronous units bear the responsibility during regular hours and multiple types of resources share the responsibility during special hours”, a coordinated and participatory operating reserve capacity allocation strategy of source, network, load, and storage is proposed, and a reserve distribution optimization model incorporating multiple types of heterogeneous resources is constructed to achieve the optimization of reserve distribution of each type of reserve resource in the whole network. The optimization model of standby distribution is constructed to incorporate multiple types of heterogeneous resources and realize the step-by-step refinement of standby resources distribution among the whole grid - resources of the same type - units/stations. A case study is carried out based on a provincial power grid, and the effectiveness of the proposed strategy is verified by comparing it with the existing operational reserve allocation guidelines.

An intelligent attacker can carefully design stealthy false data by hiding them among normal ones to escape detection. Such attacks are referred to as dummy data attacks (DDAs), whose stealth is further improved in this paper, which can induce line overloads and even cascading failures in a power system. Thus, in this paper, we propose a robust mitigation strategy model to mitigate DDAs risk by preventing the attacker from constructing high-stealth attack scenarios. The robust mitigation strategy secures line flow levels and trades off the economy and the security of power systems. A novel decomposition algorithm is developed to solve the proposed model which can guarantee the solution optimality in a finite number of iterations. Simulation results on the IEEE 118-bus and Chinese 132-bus systems demonstrate the effectiveness of the proposed approach.

In recent years, high penetration of variable generating sources, such as wind power, has challenged independent system operators (ISO) in keeping a cheap and reliable power system operation. Any deviation between expected and real wind production must be absorbed by the power system resources (reserves), which must be available and ready to be deployed in real time. To guarantee this resource availability, the system resources must be committed in advance, usually the day-ahead, by solving the so-called unit commitment (UC) problem. If the quantity of committed resources is extremely low, there will be devastating and costly consequences in the system, such as significant load shedding. On the other hand, if this quantity is extremely high, the system operation will be excessively expensive, mainly because facilities will not be fully exploited. This thesis proposes computationally efficient models for optimal day-ahead planning in (thermal) power systems to adequately face the stochastic nature of wind production in the real-time system operation. The models can support ISOs to face the new challenges in short-term planning as uncertainty increases dramatically due to the integration of variable generating resources. This thesis then tackles the UC problem in the following aspects: Power system representation: This thesis identifies drawbacks of the traditional energy-block scheduling approach, which make it unable to adequately prepare the power system to face deterministic and perfectly known events. To overcome those drawbacks, we propose the ramp-based scheduling approach that more accurately describes the system operation, thus better exploiting the system flexibility. UC computational performance: Developing more accurate models would be pointless if these models considerably increase the computational burden of the UC problem, which is already a complex integer and non-convex problem. We then devise simultaneously tight and compact formulations under the mixed-integer programming (MIP) approach. This simultaneous characteristic reinforces the convergence speed by reducing the search space (tightness) and simultaneously increasing the searching speed (compactness) with which solvers explore that reduced space. Uncertainty management in UC: By putting together the improvements in the previous two aspects, this thesis contributes to a better management of wind uncertainty in UC, even though these two aspects are in conflict and improving one often means harming the other. If compared with a traditional energy-block UC model under the stochastic (deterministic) paradigm, a stochastic (deterministic) ramp-based UC model: 1) leads to more economic operation, due to a better and more detailed system representation, while 2) being solved significantly faster, because the core of the model is built upon simultaneously tight and compact MIP formulations. To further improve the uncertainty management in the proposed ramp-based UC, we extend the formulation to a network-constrained UC with robust reserve modelling. Based on robust optimization insights, the UC solution guarantees feasibility for any realization of the uncertain wind production, within the considered uncertainty ranges. This final model remains as a pure linear MIP problem whose size does not depend on the uncertainty representation, thus avoiding the inherent computational complications of the stochastic and robust UCs commonly found in the literature.