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Modern mixed-integer quadratic solvers generally handle binary variables more efficiently than nonlinear mixed-integer solvers. This is relevant to the power system operation models as the unit commitment formulations typically contain a large number of binary variables. This paper investigates how to achieve the accuracy level close to the one of the exact nonlinear models, but by utilising convex models and solvers. The presented unit commitment model is based on a Taylor-series expansion where both the voltage magnitude and angle are quadratically constrained. To achieve high accuracy, the model takes advantage of the meshed transmission network structure that enables replacement of the quadratic inequality constraints that cause constraint relaxation errors with the linear equality constraints. Quadratic constraints to be replaced as well as the operating point parameters are determined based on the presolve. The first presented case study validates the model's accuracy and the convergence of the iterative algorithm, while the second is a non-iterative full unit commitment problem. Unit commitment results show superior accuracy and similar computation times to the existing quadratic formulations on one hand and faster computation times than the exact nonlinear polar formulation on the other. [ABSTRACT FROM AUTHOR]

To decarbonize the heating sector, residential consumers may install heat pumps. Coupled with heating loads with high thermal inertia, these thermostatically controlled loads may provide a significant source of demand side flexibility. Since the capacity of residential consumers is typically insufficient to take part in the day-ahead electricity market (DAM), aggregators act as mediators that monetize the flexibility of these loads through demand response (DR). In this paper, we study the strategic interactions between an aggregator, its consumers and the DAM using a bilevel optimization framework. The aggregator-consumer interaction is captured either as a Stackelberg or a Nash Bargaining Game, leveraging chance-constrained programming to model limited controllability of residential DR loads. The aggregator takes strategic positions in the DAM, considering the uncertainty on the market outcome, represented as a stochastic Stackelberg Game. Results show that the DR provider-aggregator cooperation may yield significant monetary benefits. The aggregator cost-effectively manages the uncertainty on the DAM outcome and the limited controllability of its consumers. The presented methodology may be used to assess the value of DR in a deregulated power system or may be directly integrated in the daily routine of DR aggregators. [ABSTRACT FROM AUTHOR]

Battery swapping stations (BSSs) have a potential of becoming a part of the electric vehicle charging infrastructure mix. A number of aggregated BSSs have a significant capacity and can act as strategic players in electricity market. This paper formulates a discrete cluster model for optimal operation of aggregated BSSs that take part in the day-ahead energy and reserve capacity markets and have sufficient combined capacity to affect market prices. In the presented bilevel structure the upper-level problem optimally defines charging and discharging schedules, as well as reserve capacity, of the aggregated BSSs, while the lower-level problem clears the market and provides locational marginal prices for each BSS, as well as the reserve capacity prices. This problem is converted into a single-level equivalent using the Karush-Kuhn-Tucker optimality conditions. The presented case study reveals that BSSs can collect significant revenues when providing reserve capacity. The main benefit of the proposed cluster model is computational tractability and solution accuracy, which depends on the resolution of the clusters. [ABSTRACT FROM AUTHOR]

Battery energy storage is becoming an important part of modern power systems. As such, its operation model needs to be integrated in the state-of-the-art market clearing, system operation, and investment models. However, models that commonly represent operation of a large-scale battery energy storage are inaccurate. A major issue is that they ignore the dependence of the charging power on the battery state of energy. Consequently, market players might suffer great monetary losses for not being able to follow their day-ahead schedule and/or deliver the scheduled reserves. In order to bridge the gap between very detailed low-level battery charging constraints and high-level battery operation models used in the literature, this paper examines a dependence of battery charging ability on its state of energy. It proposes a laboratory procedure, which can be used for any battery type and technology, to obtain this dependence. It also formulates an accurate linear battery charging model, which closely approximates the real-life battery charging constraints. The proposed battery charging model is compared against the models commonly used in the literature. Battery operation schedules obtained by all the models are compared against experimentally obtained results in order to assess the value of the proposed model in real life. [ABSTRACT FROM AUTHOR]

Innovations in information, communication, forecasting, and control technologies, along with the high investments to enhance the capabilities of active neighborhoods to participate in crucial grid balancing services like fast frequency control (FFC), have gained wide attention in the research community recently. This paper aims to provide a structured and comprehensive study of the challenges, opportunities, and potentials of active neighborhood resources, e.g., distributed energy resources (DERs) and smart flexible loads, to actively participate in FFC services. Technical details, regulatory requirements, and control strategies with practices in several countries are discussed in detail. Furthermore, the challenges and opportunities for the participation of active neighborhood resources are elaborated and categorized as technical, economic, and other challenges. Finally, a model on coordinated synthetic inertia control (SIC) is demonstrated to examine the DERs like energy storage systems, small scale wind turbine generator, photovoltaic generator diesel engine generator, electric vehicle capability to provide the fast frequency response (FFR). It is observed that proposed SIC framework is potentially capable to arrest high rate of change of frequency (RoCoF) and reduce frequency nadir at different loading conditions. The observations and discussions made in this study would be demystifying for every stakeholder of the energy community. • A comprehensive study of the potentials of active neighborhood resources is presented. • Technical, regulatory, and control practices in several countries are discussed. • Challenges and opportunities are elaborated as technical, economic, and other challenges. • A model is demonstrated to examine the DERs capability to provide the FFR. [ABSTRACT FROM AUTHOR]

Large-scale integration of wind farms causes volatile bus net injections. Although these fluctuations are anticipated, their timing, magnitude and duration cannot be predicted accurately. In order to maintain the operational reliability of the system, this uncertainty must be adequately addressed at the day-ahead generation scheduling stage. The ad-hoc reserve rules incorporated in deterministic unit commitment formulations do not adequately account for this uncertainty. Scenario-based stochastic unit commitment formulations model this uncertainty more precisely, but require computationally demanding simulations. Interval and robust optimization techniques require less computing resources, but produce overly conservative and thus expensive generation schedules. This paper proposes a transmission-constrained unit commitment formulation that improves the performance of the interval unit commitment. The uncertainty is modeled using upper and lower bounds, as in the interval formulation, but inter-hour ramp requirements are based on net load scenarios. This improved interval formulation has been tested using the IEEE RTS-96 and compared with existing stochastic, interval and robust unit commitment techniques in terms of solution robustness and cost. These results show that the proposed method outperforms the existing interval technique both in terms of cost and computing time. [ABSTRACT FROM PUBLISHER]

Energy storage can alleviate the problems that the uncertainty and variability associated with renewable energy sources such as wind and solar create in power systems. Besides applications such as frequency control, temporal arbitrage or the provision of reserve, where the location of storage is not particularly relevant, distributed storage could also be used to alleviate congestion in the transmission network. In such cases, the siting and sizing of this distributed storage is of crucial importance to its cost-effectiveness. This paper describes a three-stage planning procedure to identify the optimal locations and parameters of distributed storage units. In the first stage, the optimal storage locations and parameters are determined for each day of the year individually. In the second stage, a number of storage units is available at the locations that were identified as being optimal in the first stage, and their optimal energy and power ratings are determined. Finally, in the third stage, with both the locations and ratings fixed, the optimal operation of the storage units is simulated to quantify the benefits that they would provide by reducing congestion. The quality of the final solution is assessed by comparing it with the solution obtained at the first stage without constraints on storage sites or size. The approach is numerically tested on the IEEE RTS 96. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

This paper considers the yearly maintenance scheduling of generating units within a market environment. Each producer schedules its units' maintenance periods to maximize its revenue using a bilevel approach. The upper-level problem of this bilevel model seeks maximum revenue and contains unit scheduling constraints, while the lower-level problems represent the market clearing process under different operating conditions. This single producer maintenance problem can be recast as a mathematical program with equilibrium constraints (MPEC). Since the MPECs of all producers have to be considered simultaneously and the market clearing process is common to all of them, the proposed formulation for maintenance scheduling is an equilibrium problem with equilibrium constraints (EPEC) corresponding to a multiple-leader-common-follower game. The solution of this EPEC is a set of equilibria, in which none of the producers is able to increase its revenue unilaterally by changing the maintenance periods of its generating units. [ABSTRACT FROM PUBLISHER]

Within a yearly horizon, a transmission system operator needs to schedule the maintenance outages of the set of transmission lines due for maintenance. Facing this task, two conflicting objectives arise: on one hand, the transmission system adequacy should be preserved as much as possible, and, on the other hand, market operation should be altered in the least possible manner. To address this scheduling problem, a bilevel model is proposed whose upper-level problem schedules line maintenance outages pursuing maximum transmission capacity margin. This upper-level problem is constrained by a set of lower-level problems that represent the clearing of the market for all the time periods considered within the yearly planning horizon. This bilevel model is conveniently converted into a nonlinear mathematical program with equilibrium constraints (MPEC) that can be recast as a mixed-integer linear programming problem solvable with currently available branch-and-cut techniques. [ABSTRACT FROM PUBLISHER]

Massive integration of renewable energy resources calls for new operating and planning paradigms, which address reduced controllability and increased uncertainty on the generation side. On the other hand, emerging energy storage technologies can provide additional flexibility. Therefore, generation and storage expansion models need to be coordinated to ensure sufficiency of system-wide response capabilities within different regulation intervals. This paper proposes a coordinated generation and storage expansion formulation considering primary frequency response constraints. This is a stochastic mixed-integer linear program solved using an off-the-shelf solver. The proposed formulation is compared to the case when primary frequency response is neglected. The case study performed for an eight-zone ISO New England test system quantifies the value of energy storage simultaneously providing primary frequency response and spatio-temporal arbitrage. [ABSTRACT FROM AUTHOR]

We introduce emerging utility-scale energy storage (e.g., batteries) as part of the set of control measures in a corrective form of the security-constrained unit commitment (SCUC) problem. This enhanced SCUC (ESCUC) leverages utility-scale energy storage for multiple applications. In the base case, the storage units are optimally charged and discharged to realize economic operation. Immediately following a contingency, the injections of storage units are adjusted almost instantly to alleviate short-term emergency overloads, thereby avoiding potential cascading outages and giving slow ramping generating units time to adjust their output. The ESCUC is a large two-stage mixed-integer programming problem. A Benders decomposition has been developed to solve this problem. In order to achieve computational tractability, we present several acceleration techniques to improve the convergence of the proposed algorithm. Case studies on the RTS-79 and RTS-96 systems demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models ‘decouple’ individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits. [ABSTRACT FROM AUTHOR]

This paper describes the development of a modern power system laboratory within a Bologna Declaration compliant curriculum at the Department of Energy and Power Systems, Faculty of Electrical Engineering and Computing, University of Zagreb. The paper describes laboratory components and provides insight into the experiments that students are required to perform. The laboratory course is divided into three major components: power system simulations, computer simulations and high voltage. Power system simulations are performed on a miniature real-world power system that can be synchronized to the actual power system. Computer simulations are performed both on commercially available software solutions commonly used by power utilities and consulting companies, and on software tools developed at the Department of Energy and Power Systems. High voltage exercises are performed in the High Voltage Laboratory, where students are familiarized with high voltage phenomena. Results of student questionnaires collected over the previous four years are analyzed. This feedback, together with industry experts' observations and suggestions, provides the basis for improvements that are constantly introduced. [ABSTRACT FROM PUBLISHER]

This paper presents a nonlinear dynamic simulation model of an ultracapacitor (UC) bank and the associated control system. The control system at hand consists of two levels: the lower level controls the inverter of the UC bank, while the upper control level is responsible for providing charging/discharging active power set points to be followed by the lower control level. This paper focuses on the development of the upper control level for frequency control. Specifically, two simulation case studies are developed so as to assess the performance of the proposed control framework. In the first case study the upper control level is developed using a classical Proportional-Integral-Derivative (PID) controller. In the second case study the upper control level is devised using a Model Predictive Control (MPC) algorithm based on internal linear prediction model of a nonlinear UC bank. In both cases, a nonlinear UC bank simulation model is used. The simulation case studies are modelled and tested in Matlab/Simulink. The response of the MPC-controlled UC bank is compared to the 3 existing PID-control algorithms for frequency control. The simulation results show that the MPC algorithm outperforms the conventional PID controllers. [ABSTRACT FROM AUTHOR]

Suitably located energy storage systems are able to collect significant revenue through spatiotemporal arbitrage in congested transmission networks. However, transmission capacity expansion can significantly reduce or eliminate this source of revenue. Investment decisions by merchant storage operators must, therefore, account for the consequences of potential investments in transmission capacity by central planners. This paper presents a tri-level model to co-optimize merchant electrochemical storage siting and sizing with centralized transmission expansion planning. The upper level takes the merchant storage owner's perspective and aims to maximize the lifetime profits of the storage, while ensuring a given rate of return on investments. The middle level optimizes centralized decisions about transmission expansion. The lower level simulates market clearing. The proposed model is recast as a bi-level equivalent, which is solved using the column-and-constraint generation technique. A case study based on a 240-bus, 448-line testbed of the Western Electricity Coordinating Council interconnection demonstrates the usefulness of the proposed tri-level model. [ABSTRACT FROM PUBLISHER]

Energy storage (ES) is a pivotal technology for dealing with the challenges caused by the integration of renewable energy sources. It is expected that a decrease in the capital cost of storage will eventually spur the deployment of large amounts of ES. These devices will provide transmission services, such as spatiotemporal energy arbitrage, i.e., storing surplus energy from intermittent renewable sources for later use by loads while reducing the congestion in the transmission network. This paper proposes a bilevel program that determines the optimal location and size of storage devices to perform this spatiotemporal energy arbitrage. This method aims to simultaneously reduce the system-wide operating cost and the cost of investments in ES while ensuring that merchant storage devices collect sufficient profits to fully recover their investment cost. The usefulness of the proposed method is illustrated using a representative case study of the ISO New England system with a prospective wind generation portfolio. [ABSTRACT FROM AUTHOR]

The integrated energy system is considered to be an important way to avoid energy supply risks by virtue of advantages in meeting diversified energy demand and improving energy utilization efficiency. Energy storage enables microgrid operators to respond to variability or loss of generation sources. In view of the difficulty of battery to fully improve the energy utilization efficiency and solve the problems of clean energy power large-scale consumption, and the difficulty of single demand response to significantly improve the operational reliability and economy of the integrated energy system, a rolling optimization planning framework and model of an integrated energy system considering compressed air energy storage and sliding time window-based electric and heating integrated response demand is proposed, which can obtain both optimal resource configuration and energy management strategy. The planning problem is to coordinate the output of various dispatching resources on the basis of satisfying the constraints, so as to minimize the total cost. Taking into account the uncertainty of wind power and photovoltaic output and the unplanned outage risk of the unit, a stochastic optimization model is constructed based on the probability density function of the risk variable, and finally the cost probability distribution is obtained. The results verify the universality of the planning model. The comparison of five different energy system configuration schemes and multiple time window length schemes verifies the validity and superiority of the model. The simulation results show that the integrated energy system scheme proposed by this planning model has better economy than the scheme without compressed air energy storage, and the operating cost decreases by 11.9% and the total cost decreases by 4.5%. In addition, the results show that through the introduction of demand response, the operating economy and flexibility of the integrated energy system are improved, and as the sliding window length increases, the total cost gradually decreases. • A rolling optimization planning framework and model of an IES considering CAES and sliding time window is proposed. • Integrated demand response (DR) is introduced into IES model. • The comparison of five different energy system configuration schemes verifies the validity and superiority of the model. • The operation characteristics of electricity-thermal joint storage and joint supply of CAES can improve the economy of IES. • A stochastic optimization model of IES is constructed based on Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

The software package described in this paper, Pyramidia, combines two strong elements into one lecturing system. The first is the simple addition of content and interactivity to existing PowerPoint presentations, and the second is video capture of a lecture. These features provide obvious benefits for both sides of the lecturing process: the lecturers and the students. Students can re-experience lectures at anytime, anywhere and at their own pace. Lecturers can add interactivity to their lectures without worrying about learning complex new software. Pyramidia is a descendant of previous software developments and research on ICT usage in education, and has been used and tested throughout a few semesters of lectures for several courses. Although there are many solutions, both commercial and academic, available to potential users, Pyramidia is unique for its .NET framework programming base. [ABSTRACT FROM AUTHOR]

Accurate electric vehicle (EV) powertrain modeling, simulation and validation is paramount for critical design and control decisions in high performance vehicle designs. Described in this paper is a methodology for the design and development of EV powertrain through modeling, simulation and validation on a real-world vehicle system with detailed analysis of the results. Although simulation of EV powertrains in software simulation environments plays a significant role in the design and development of EVs, validating these models on the real-world vehicle systems plays an equally important role in improving the overall vehicle reliability, safety and performance. This modeling approach leverages the use of MATLAB/Simulink software for the modeling and simulation of an EV powertrain, augmented by simultaneously validating the modeling results on a real-world vehicle which is performance tested on a chassis dynamometer. The combination of these modeling techniques and real-world validation demonstrates a methodology for a cost effective means of rapidly developing and validating high performance EV powertrains, filling the literature gaps in how these modeling methodologies can be carried out in a research framework. [ABSTRACT FROM AUTHOR]