Having the necessary infrastructure, the daily routine of system operators (SOs) starts the day before dispatching by scheduling the generators to meet the predicted demand. The task of scheduling the generators depends on the structure of the market and the physical limitations of the system. In vertically integrated companies the decision is based mainly in efficiency while in deregulated markets prices play an important role, in any case the physical limitations of the system must be taken into account. This day ahead planning sets a baseline for the operation in real time but is not strictly fulfilled, as predictions are not exact as demand varies freely in most cases. In order to match generation and demand new predictions are done hours or minutes before dispatching and the base scheduling is modified. However at the moment of dispatching there will still be differences between generation and demand, and some generators with reserved **power** capacity, will be responsible of correcting these small differences by continuously modifying their output. This task is commonly known as frequency regulation because these imbalances are indirectly calculated by measuring the deviations of the frequency of its nominal value (50Hz or 60Hz depending on the system). If frequency exceeds its nominal value it means that there is over generation and generators receive the order of decreasing their output, and vice versa. To perform this task efficiently SOs must know in advance how frequency changes as a function of the imbalance between generation and demand, f(∆P ), for the particular setup of the grid. Using the inverse of this function the SO can calculate the needed change in generation by measuring the deviation of the frequency from its nominal value.

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The UC problem aims at determining the schedule combination of the available generating units in order to satisfy the forecasted **power** demand at the minimum operating cost. The output of the UC is the commitment status and generation dis- patch of various generating units satisfying system-wide constraints such as **power** balance and specific unit constraints such as capacity, ramping limits and mini- mum up and down times [1]. The transmission-constrained UC problem generalizes the UC problem to take into account constraints on the flows in the transmission network. The UC problem without commitment decisions is often called economic dispatch (ED) or optimal **power** flow (OPF) if the network is considered. The ab- sence of commitment decision does not mean that the generating units are free to decide their schedule. Instead, their on/off status are considered to be fixed in advance. Th OPF has been one of the most important and widely studied non- linear optimization problems in the last decades and a wide range of approaches and solution methods can be found in literature. A review of the solution methods applied to the traditional OPF problem can be found in [2–5], and more recent reviews include non-deterministic approaches [6–8]. The analysis of the effects of the convex relaxations and linear approximations for the OPF is an essential key in the performance of the OPF and a number of authors provide useful information about it [9–14]. The OPF is a non-linear optimization model due to the non-linear constraints of the alternating current (AC) **power** flow equations. Thus, if the bi- nary variables that decide the scheduling of the generating units are included along with the non-linear constraints of the AC **power** flow to solve the UC problem, the optimization model becomes a highly non-convex problem which is very difficult to solve.

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Depending on the aim of the resilience study, the performance of **electric** **power** **systems** can be measured using numerous different metrics. Two measurements that are able to describe the impact of the extreme event within a time frame are the Expected Energy Not Supplied (EENS) and the Energy Index of Unreliability (EIU) (R. Billinton and W. Li, 1994, R. Allan and R. Billinton, 2000). The first, shown in Equation 4.5, indicates how much service (energy) was not provided during the studied time period as an absolute number (MWh or GWh). The second, shown in Equation 4.6, is directly related to EENS, which is normalized using the total energy demand in the studied time frame (%). In the following equations, 𝐸 ! is the energy not supplied with a probability 𝑝 ! of occurrence of scenario k during the time frame of the study. E represents the energy demand in the whole study period.

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frequency control of conventional generation units [40]. The hidden inertia emulation control usually includes two different loops: one considering the RoCoF and the other considering the frequency excursion (∆p ∝ RoCoF & ∆ f ) [41–43]. However, there are also proposals to use only one additional loop, being ∆p ∝ RoCoF [44–46]. Even though these methods improved the nadir frequency (minimum value), a little frequency dip was observed in later stages. This was due to a small reduction in the generated **power** compared to the prefault active **power** (thus, not operating in the MPP) [47]. The fast **power** reserve approach defines the overproduction **power** ∆p as a constant value independent of the **power** system configuration and frequency deviation [48–51] or as a variable value depending on the frequency deviation or minimum rotor speed limits [52–54]. With these three techniques, as the additional **power** ∆ p is provided, the rotor and generator rotational speeds decrease (subsequently, modifying their torques). Rotor speed variations cause large amplitude edgewise vibrations for the blades [55], affecting the productivity and reducing the efficiency [56]. Large torque increases can address severe mechanical loads on the turbine, even causing critical situations under high mechanical stress conditions [57]. Moreover, consecutive torque increments is related to random load cycles, with important influences on fatigue loads [58].

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The second two-month period is used to perform laboratory tests in each component of the system, so as to acquire a deep knowledge and characterize them in order to be able to improve them or to take into account their particular behavior for the next innovation stage. The third period consists of different partial competitions each one concerning only one system (PV generation or pumping control). In order to prepare each event the students must face a creative practice in which innovative competences are trained. This way the subject allows the students, for example, to learn about the grid connection of a PV generator and to program the Maximum **Power** Point Tracking (MPPT) along the sun day, by innovating and practicing with a real installation together with the motivation of a teamwork facing a contest. In this period, predictive models of PV generation, which are prepared by the students under the supervision of experts in statistics, are also used in order to improve the success options for the competitions.

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Regarding the point of connection, the maximum **power** that an EV can supply, or absorb, is conditioned by the location of the charging point. In the simplest case, the EV location is a known parameter of the problem. On the other hand, to account for the uncertainties in EV owner behaviour, Monte Carlo simulations or Markov chains based techniques [54] can be employed to determine the node of connection to the grid. In this last case, each EV is assumed to have a certain “state”, e.g. driving or charging, and the transition from one state to another is carried out according to transition probabilities. These probabilities are determined by statistically analysing some data extracted from surveys or mobility studies. For instance, it is highly probable that the EV stays at home during night hours or that it performs the first departure in the early hours of the morning. Regardless of the way of modelling EV movement, it is necessary to take into account those time periods in which EVs are moving or not connected to the grid since it may be necessary to update the state of charge despite EV not being associated to any node.

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On the other hand, the technical literature pertaining to the operation of CSP plants is modest. The pioneering work reported in [111] analyzes the value of CSP plants and TES in various regions of the US and characterizes the profitability of CSP plants using a mixed-integer linear programming model, whose inputs come from a specialized software. The thermal **power** produced by the SF in each hour and the market prices are input data to the model. Using this information, [111] reports results from several studies to assess the profits obtained by a CSP plant with TES when participating in the pool, the impact on the profits of the optimization horizon and the impact of partici- pating in reserve markets, among others. On the other hand, reference [40] proposes a model to build strategic offering curves for a CSP plant which in- corporates a TES. The model is formulated as a mixed-integer linear stochastic programming problem, where the thermal **power** production from the SF and the market prices are considered as uncertain parameters. The uncertainty of the solar resource availability is modeled using robust optimization [24, 25], whereas the variability of market prices are represented via scenarios [26].

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This event may be explained because: for the date the measurement was registered no voltage fluctuation was noted; also, the loads applied to the three phases were basically resistive elements. Consequently, no difference was observed between input and output voltage, which guaranteed the stability of the test in question on voltage registry and control. Similarly, it was established that the only variable in the system is associated to **electric** **power** consumption and the variables inherent to the behavior of the **electric** elements.

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The economic principle behind a competitive price system is to deliver the proper signals to market agents. Individual’s consumption, production and investment decisions are adapted to the interaction with other economic agents through price. Nevertheless, in **electric** **power** markets, typically most consumers are kept under a flat regulated tariff, disconnected from wholesale prices that vary hourly depending on consumption and supply. This partial disconnection between the price signals at which producers sell in the wholesale market and the price signal seen by customers has been identified by many authors as an important source of economic inefficiency (Rosenzweig, Fraser, Falk, & Voll, 2003; Borenstein & Holland, 2005; King, King, & Rosenzweig, 2007; Chao, 2011).

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This problem has also been widely discussed in literature (Padhy, 2004; Saravanan et al., 2013) and is actively researched, as better solutions mean less costly dispatch. Minor differences, e.g. 0.5% reduction of fuel use, can result in savings of million dolars per years to large utilities (C.-A. Li et al., 1997). UC is a mixed integer linear program (MILP) by nature hard to solve, due to its high dimensionality and the use of binary variables, as the solution states which unit is either on or off in each time period. This increases the possible combination of feasible solutions of the UC making hard to find optimal solutions, and added to the different constraints considered to model the **systems**, such as thermal generator constraints or reserve constraints, increases its complexity even more. Various approaches have been developed, which ranges from complex mathematical optimization models to simple “rule-of-thumb” methods, e.g. priority lists.

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A constrained electricity infrastructure may partially limit the validity of the **power** potential assessment based exclusively on current velocities and hydrodynamic impacts. This work investigates the voltage impact of three scales of tidal plants in Chacao channel by modeling the **power** extraction using horizontal axis turbines. The **power** flow through the transmission network is solved using a nested Newton-Raphson **power** flow solver. The current velocity data used for modelling the **power** extraction is obtained from direct measurements using ADCPs and hydrodynamic modelling.

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Distributed generation can support voltage, reduce losses, provide backup **power**, improve local **power** quality and reliability, provide ancillary services, and defer distribution system upgrade [4.1]-[4.3]. Modeling of renewable generation raises several challenges to distribution load flow calculations since capabilities for representing intermittent generators, voltage-control equipment, or multi-phase unbalanced **systems** are required. In addition, studies of **systems** with intermittent non- dispatchable resources will require a probabilistic approach and calculations performed over an arbitrary time period that may range from minutes to years. Load representation is another important issue since voltage-dependent loads with random variation must be also accounted for. These issues complicate the study of **power** distribution **systems** since software tools have to combine new analysis capabilities with a high number of models for representing various generation technologies, besides the conventional distribution system components, and include capabilities for time-driven calculations [4.4].

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Some estimation can now be easily made. In case of a bus (car) with 40 kW engine, the energy consumption in 1 hr is 40 kWh; for recharging in 6 hrs the PVCs **power** of 6.7 kW is needed. The average price of PVCs is now around $4 (US dollars) per W; it is constantly decreasing (contrary to the traditional fuel’s cost), and the minimal price in the market today is less than $2/W [9]; the information about prices of $1/W appears. Taking that PVCs utility life is 20 years and observing the existing tendencies on the market, we can safely use the price of $2/W, which gives for the 6.7 kW PVC system the total cost of $13,400. The existing electrical energy cost in México and USA is around $0.1/kWh, and not less than 50% growth is expected in the next 20 years; thus 40 kWh of consumption gives $4/day, which will amount to $24,000 in 20 years, much more than the PVC’s cost. As we see, the use of solar **electric** vehicles certainly needs initial investment, but quite soon it will be justified not just ecologically, but economically as well. On the other hand, a bus with 25 m 2 roof surface can carry autonomous PVC set on the roof, thus increasing the total recharging time; to generate a **power** of 6.7 kW, the PVCs must have efficiency of around 26 %. It is higher than efficiency of average commercial PVC, but taking into account the Sun tracking effects and the existing tendencies of development of PVCs, we can think about this option as quite viable. Thus, the two options of recharging might be considered: the stationary grid-connected PVC set at the place where vehicles are “resting”, and the autonomous on- board PVC system. In the first case, more economic PVCs with average efficiency can be used, also a DC-AC inverter is needed to make the parameters of generated electrical energy compatible with those of grid; in the second case the inverters might not be needed, but there will be strong demands towards efficiency of PVCs. In both cases, Sun tracking gives additional advantages.

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An **electric** arc furnace of 75 MW for steel melting was connected at 132 kV voltage level in the Transmission System of Buenos Aires (Argentina). A second **electric** arc furnace of the same **power** rating will be connected in the future. The IITREE has made measurements of some **power** quality indicators to check the reference levels at the Point of Common Coupling (PCC) and the emission levels of the arc furnace as a disturbing load. The quantities measured are voltage and current harmonics, Flicker, **power** factor and active and reactive **power**.

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This paper describes the aspects of **power** quality at the point of common coupling (PCC) where an arc furnace for steel melting with alternating current is connected. By measurements of flicker, harmonics content in voltage and current, active and reactive **power** and **power** factor, the preservation of the reference levels for the supply voltage and emission limits for the furnace as a customer are evaluated.

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Comparing the two expressions of the instantaneous reactive **power** of the balanced voltages system and the unbalanced voltages system, we find terms which only arise for unbalanced **systems**. These are therefore due to unbalances in the system. As Steinmetz states, reactive **power** is quantified as the amplitude of the flows of instantaneous reactive **power**. We have performed simulations by using OrCAD, a program for **electric** circuit modeling and simulating used by numerous authors [35–38], in order to contrast the mathematical model proposed in the article and experimental measures. 2. Methodology

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Cases of cross-border policy and technical alignment depend to a large extent on the size of the countries concerned, their history of co-operation with each other and indeed whether they are land-locked. The internal geography of a Member State can have a significant influence, simply due to the availability of natural resources and their physical impact on transmission grid **systems**. For example, in Sweden and Finland most hydropower generating capacity is in the north, so it supplies the more densely populated southern regions via long transmission lines, however nuclear stations are located in the south. Whereas in Germany, a hub-system model exists, arising from the heavy concentration of production plants in particular areas with the a consequent development of transmission **systems** from these areas.

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[18] M. Singh, V. Khadkikar, and A. Chandra, “Grid synchronisation with harmo- nics and reactive **power** compensation capability of a permanent magnet synchro- nous generator-based variable speed wind energy conversion system,” IET **Power** Electronics, vol. 4, no. 1, pp. 122 – 130, January 2011.

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A new subject called “Devising an **Electric** **Power** System” has been presented including its conception, the development along the course and the results obtained after the first year of implementation in 2014-15. Following the CDIO concept and fully based on laboratory work, the starting point for this experience was to conceive a physical platform which would allow the students to learn through the innovation, all facilitated by the ability of testing any improvement any time is needed. The strength of the new methodology presented is putting together the laboratory work (better “doing” than only “listening/studying”) and the

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As a vital part in smart grid, demand response supports the restoration of balance between electricity demand and supply. This concept is highlighted by (Yao et al., 2017), where a real time charging scheme is proposed to coordinate the EV charging loads based on the dynamic electricity tariff. By the other hand, an optimization problem is formulated to maximize the number of EVs selected for charging at each time period. Two objective functions are in conflict: maximizing the EV owner’s convenience in meeting all charging requests and minimizing the total electricity bill for the parking station. Similar contributions are presented in (Lu et al., 2017), focused on the real time interactions between energy supplier and the EVs users in a fully distributed system in which the only information available to the end users is the current price. In this sense, a real time charging pricing algorithm is introduced to maximise the aggregate utility of all the EVs users and minimise the electricity cost generated by the energy supplier. In addition, the EVs users and the energy supplier interact each other running the distributed algorithm to find the optimal **power** consumption level, and the optimal price values to be revealed by the energy supplier, in order to adapt the users’ demands constantly and maximise their own utility. Another study in (Pal and Kumar, 2017) is presented in this context, defining demand response as “voluntary change of demand”, proposing an approach to enable the EVs smart charging technology among residential customers. This propose incorporates operation and analysis of **power** transaction between the energy user and the electricity grid, including the concept of the **power** sharing among neighbours in the residential demand response framework.

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