Flexibility in future power systems with high renewable penetration: A review by Kanaga Gnana

Renewable and Sustainable Energy Reviews 57 (2016) 1186–1193

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Flexibility in future power systems with high renewable penetration: A review M.I. Alizadeh a, M. Parsa Moghaddam a,n, N. Amjady b, P. Siano c, M.K. Sheikh-El-Eslami a a

Faculty of Electrical & Computer Engineering, Tarbiat Modares University, Tehran, Iran Department of Electrical Engineering, Semnan University, Semnan, Iran c Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, SA 84084, Italy b

art ic l e i nf o

a b s t r a c t

Article history: Received 4 March 2015 Received in revised form 23 September 2015 Accepted 17 December 2015

Renewables are going to make our planet a better place to live. These clean resources of energy can bring a handful of advantages to the future electricity industries. Nevertheless, the large percentage of renewables integration can cause some operational issues, in power systems, which are needed to be identified and coped with. This paper defines, classifies and discusses the latest flexibility treatments in power system based on a comprehensive literature study. The current work specifically considers the abilities, barriers, and inherent attributes of power systems’ potential to deal with high integration of Variable Generations (VGs) in future flexible power systems. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Demand side management Energy hubs Fast Response Resources (FRR) Flexibility Multi-carrier energy systems Smart grid Variable Generation (VG)

Contents 1. 2. 3. 4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multi-carrier energy systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flexibility impacts on power systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Super short-term impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Short-term impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Mid-term impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Long-term impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Improving ﬂexibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Flexibility barriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Certainly, the future is uncertain. Due to transforming of technologies, increasing concerns of the societies, and climate changes, dealing with ﬂexibility issues seems crucial for almost all

Corresponding author. Tel.: þ 98 21 82883369; fax: þ 98 21 82884325. E-mail address: parsa@modares.ac.ir (M. Parsa Moghaddam).

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scientific fields. From communication network, manufacturing, oil extraction and transportation to defense systems and energy production, transmission and distribution, flexibility will be a must in near future. Hence, a wide range of stakeholders are involved with the matter [1]. Manufacturing, among all, due to

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competitiveness in producing low cost and high quality products to respond quickly to the rapidly changing markets, flexibility, as one of the most sought-after properties, has been in the warmth of the spotlight for years [2]. Accordingly, flexible Manufacturing Systems (FMS) are introduced. FMS is a manufacturing system in which there is some amount of flexibility that allows the system to react in case of changes, whether predicted or unpredicted [2]. Similarly, flexibility, in transportation, is defined as "the adaptability of a system in response to external changes, while maintaining satisfactory system performance". System performance is characterized by parameters such as capacity, level of service, maintainability, and profitability. External changes are referable to uncontrolled conditions of a system, including deviations in level of demand or use, changes in spatial traffic patterns, infrastructure loss and degradation, and volatilities in the price and availability of important resources such as fuel, etc. [3]. As it can be seen, flexibility has no general definition and is highly dependent on the system under study. Accordingly, is seems irrational to state a same definition for power system flexibility or any other engineering subjects. The general flexibility terms for different industries, however, can be applied to the power system flexibility assessment. The current paper tries to gather general flexibility terms applied in many different industries to be adopted in power system flexibility assessments. Due to the increasing penetration rate of variable generation resources integration into the power generation portfolios, a flexibility assessment looks necessary because of the rising issues in both operational and planning horizons of the power system. Accordingly, these fields are studied separately. Hence, to cover versatile aspects of flexibility in power systems the following tasks are done. First, flexibility from multi-carrier energy systems’ point of view, as a general system is discussed in Section 2. A comprehensive literature review of recent researches in the field of flexibility in power systems is, then, presented in Section 3. The two subsequent Sections, 4 and 5, are dedicated to answer the two following major questions targeted to clarify the flexibility features in power systems: why flexibility assessment in power system is needed? and how can flexibility be improved through available resources? Finally, Section 6 is discusses the current barriers faced by power system operators and planners both to unleash the hidden potentials of the available flexibility resources and to plan for flexible future grids, respectively.

2. Multi-carrier energy systems Multi (Carrier) Energy Systems (MES) as one of the premier systems dealing with energy in a wider range than power systems are involved with the flexibility in many features [4,5]. Reference [4] states that MES can enhance technical, economic, and environmental performance of energy systems through energy hubs, multi-energy microgrids, and Virtual Power Plants (VPP). Energy hubs can increase the flexibility of energy systems through coupling of supply and demand consistently for design purposes aimed at minimizing investment or life cycle costs [6]. Multienergy microgrids can similarly enhance flexibility through versatile capabilities such as energy provision by trigeneration applications namely, electricity, cooling and heating for controllable multi-energy loads, different aggregation levels, namely local level, multiple building and power grid, and participating in distributed markets and power grid exchanges by making use of automatic iterative feedback signals [4,7]. VPPs are defined as flexible aggregations of coordinated distributed energy resources in an optimal pattern, capable to play in the energy markets, and offer services in the same way as conventional power plants. It is worth noting that VPPs, both of

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commercial and technical types, can deploy the synergies between electricity and other energy vectors for system balancing purposes in presence of fluctuating Variable Generations (VGs). As a result, it can be inferred that VPPs can be counted among the main flexibility providers of the future power grids. MES can increase the energy system flexibility for instance through allowing to take advance from the storage characteristics of thermal loads supplied by electricity, or by exploiting combined production of electricity, heat/steam [8], energy transportation [9], cooling and water with focus on agro-food industries [7]. In addition, MES can also offer the energy flexibility provided by Combined Heat and Power (CHP) plants buffered by thermal storages. Generally, the flexibility improvements allowed by MESs are mainly due to the multiple alternatives they can offer such as versatilities from spatial, multi-service, multi-fuel, and network perspectives. According to [4], the goal of flexibility is to consider the robustness of optimal operation and design variables to changing boundary conditions. It is notable that flexibility does not mean the robust operation against any possible uncertainties and fluctuations. It, however, represents a compromise between flexibility worth and flexibility cost.

3. Literature review Power system flexibility consideration has been around since the integration of VGs in bulk power generation mix. One of the premier definition is announced by [10], as "the ability of a system to deploy its resources to respond to changes in the net load, where net load is defined as the remaining system load not served by variable generation". Flexibility, however, remained negligible due to very limited penetration rate of non-dispatchable energy resources. The variability of the wind power and other renewables is presented in [11], as in other early reports released in this area. These reports indicate that non-dispatchable energy resources, due to their natural cycles, can fluctuate over short timescales intraday and intra-hourly and require different management strategies. These management strategies are briefly aggregated as follows; increasing grid capacity and cross border connections, developing cost-effective balancing and regulating markets with transparent gate-closure times also reflecting the technical and economic needs on the system, enhancing uptake of efficient demand-side response mechanism, installing more flexible generation capacity, utilizing a mixture of different renewable energy resources, improving forecasting and modeling of natural fluctuations and increasing utilization of communication technologies to disseminate this information between grid operators and markets [11]. The term power system flexibility is also used in [12]. According to [12], a power system is called flexible if it can – within the economic boundaries – rapidly respond to large fluctuations in demand and supply, both scheduled and unforeseen variations and events, ramping down production when demand decreases, and upwards when it increases. The report also suggests that in order to have a flexible power system three main steps must be accomplished namely, flexibility resource identification, accounting the existence flexibility requirements, and considering the Net Flexibility Resource (NFR). It adds that, once the existing flexibility requirement has been accounted for, the remaining net flexibility resource can be considered to be available for use in balancing the additional fluctuations introduced by additional VGs output. As it was mentioned in the first section, flexibility needs to be measured. In this regard, [13] presented a framework to measure flexibility for the first time. Reference [13] declares that magnitude, ramp response, frequency, and detecting available flexible resources characterize the supply and demand imbalances, which

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are indications of the system needs for flexible resources. Magnitude refers to both the size of ramp events and the direction of that event while, ramp response refers to both the rate of change of the net load or unit output and their predictability. In addition, due to the probabilistic nature of supply and demand imbalances, frequency of the events is also required to characterize the flexibility. Frequency refers to the number of times of events with various magnitudes and responsiveness. In addition, available flexible resources determine the ability to change resources in response to imbalances between net load and total resources. Reference [13] also verifies that flexibility assessment is not yet a common practice due to the lack of indices able to measure flexibility. It is worth noting that, numerous researches [3,8,14–20] investigated the effects of integrating flexible resources namely, demand side resources, energy storage systems, flexible thermal generating units, non-thermal units, in enhancing the power system's ability to cope with high VG integration. In addition, variable generation attributes, apart from operational impacts on power system, are also practically investigated in a considerable portion of recently published papers, focusing on ramp detection methods [6,15,21–27]. The major wind characteristics described in these references are ramping events, stochastic behavior, variability, temporal correlation, and spatial correlation. Reference [15] applied Swinging Door Algorithm (SDA), a piecewise linear data mining technique, to detect the ramp events from historical data. It is mentioned that ramp events are typically extracted through a piece-wise linear approximation of the original time series of data. It is added that a ramp event is characterized by its instantaneous rate of change, its derivative, and is approximated initially by a local ratio of difference. In [22], two ramp rate definitions are presented based on wind power deviation from a pre specified threshold and the lower and upper values of wind generation between the two endpoints. Wind ramp and net load ramp events’ characteristics are investigated in [23]. In [23] it is mentioned that two following situation are the concern of power system operators as worse ramp events, namely wind down-ramps that happen at the same time as load up-ramps and wind-up ramps that happen at the same time as load down-ramps. In [24], data mining techniques are applied to identify and classify the wind ramp events according to predefined thresholds. A feature selection approach based on mutual information is employed to optimize the input data to the classifier. Support Vector Machine (SVM) is then applied as a classifier. Flexibility assessment is studied in [28] where the authors use simple accounting in order to estimate the maximum ramping capabilities for a system and relies on system-specific judgment to qualitatively assess how much of this maximum is actually available. Flexibility assessment is done based on an identification algorithm with four sequential steps. Step one detects the nominal available flexible resources, out of dispatchable plants, storage facilities, flexibility provision through interconnections, and demand side resources. Step two defines how much flexibility is deployable according to power system constraints. Step three identifies how much flexibility is required. At last, step four determines how much flexibility the existing flexible resources can provide at most. Flexibility ASsessmenT (FAST), however, does not present a qualitative method for assessing system flexibility adequacy [28]. Nevertheless, [29] presents a new probabilistic index to measure power system flexibility in long-term planning problems namely, Insufficient Ramp Resource Expectation (IRRE). IRRE is the expected number of observations when a power system cannot cope with the changes in net load, predicted or unpredicted. Calculation of the IRRE follows a similar structure of the Loss of Load Expectation (LOLE), however, rather than forming a distribution of the unavailable generation capacity, a distribution

of the available flexible resources is formed for each direction and time horizon. The metric estimates the expected percentage of ramping events during a year that exceed power system's capabilities. Flexibility assessment in long-term is also investigated in [9] by introducing a framework able to assess the operational flexibility impacts on the capacity planning in a single, monolithic optimization problem. Directly including flexibility within planning of power systems enables optimizing for flexibility configurations. Such a flexibility screening can produce generation mixes that are lower cost and/or more reliable in actual operations, even though they might have been eliminated as sub-optimal using today's simpler screening tools. Flexibility assessment in the short term is, instead, investigated in [30]. A relative deterministic flexibility index is presented in [30] based on the ability of part-loaded synchronized and quick startup/shut down generators to provide flexibility. The metric is obtained based on normalized both minimum/maximum generation capacities and their available ramp rates. Due to a tight connection between long-term capacity planning and the corresponding short-term operational decisions, the paper presents a Unit Construction and Commitment (UCC). The UCC is based on the unit commitment (UC) algorithm which enforces the flexibility constraints, such as ramping rate, minimum stable generation, and minimum up/down time. The optimization thus determines whether a plant should be built to provide additional flexibility at a reasonable cost and the optimization horizon must be sufficiently long in order to capture the intraday, daily, and seasonal variations in load and wind generation that drive the need for flexibility. The considerable issue in developing flexibility metrics is majorly due to the fact that inadequate available ramp capacities may lead to forced load shedding or VG output curtailment. Forced load shedding due to ramp capacity scarcity implies that the statistical attributes of a probabilistic flexibility metric and reliability metric Loss of Load Probability (LOLP) may be similar [31]. On the other hand, wind power curtailment can be used when wind levels are higher than wind speed forecast or demand is lower than expected. In addition to correcting day ahead forecast errors, curtailment can also be used to reduce the rate of thermal plant ramp down in the event of rapid increases of available wind speed. However, curtailment only helps for these downward flexibility requirements. Regarding to this, [31] presents a Loss of Wind Estimation (LOWE) probabilistic metric. The idea is based on the annual percentage of wind power curtailment due to the lack of system ramp capacity. It is stated that wind curtailments (sheddings) may occur in the following situations:

net demand is lower than the Minimum Load Level (MLL) of system;

net demand drops sharply but committed generators do not

have sufficient ramp-down rate or cannot be shut down immediately; and, net demand increases sharply but committed generators do not have sufficient ramp-up rate or offline generators cannot start up immediately.

According to these situations, Probability Density Function (PDF) of wind curtailment can be extracted. Flexibility trinity concept is deﬁned, in [32] as a set of metrics to present the available operational ﬂexibility of a single power system unit and for the whole power system. Flexibility trinity characterizes power systems based on four capabilities, namely power capability for up/down regulation, energy storage capability, rampable power capability, and power ramping duration, where the duration is dependent on both power capability and

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power ramping capability. In [32], the ﬂexibility of each power system is related to its capabilities in energy in-feed/out-feed into the grid. This concept is proposed based on the fact that high penetration rate of variable Renewable Energy Sources (RES) in many countries has led to signiﬁcant relative and absolute shares of stochastic power generation [32]. It can be concluded from the literature review that the following research gaps in this area exist.

control, ramp rate control, power electronic control (governor response and initial response) [30,33], and dynamic modeling of large wind plants (prime mover dynamics). It is stated in that most of the recent attention has been focused on the dynamic modeling of large wind plants to study the impact on power system transient stability [34].

A comprehensive ﬂexibility deﬁnition to cover all aspects of this

Three major flexibility impacts in short-time horizon are imaginable, namely, increasing ramping requirements, increasing reserve needs, and minimum output limits [6,33,35]. The increase in hour-to-hour net load changes and related sub-hourly dynamics require forcing thermal plants, especially steam units, up against their ramp rate constraints. Accordingly, additional flexible combined cycle units and combustion turbine are needed in order to accommodate ramping requirements. Reliable power system operation requires proper reserve allocation due to uncertainty in demand and in availability of supply. In addition, the ability of a generating unit to provide reserves is a function of its ramping capability over the corresponding timeframes. Reserves are the extra capacity maintained to account for the possibility that there may be insufficient generating capability when demand is higher than expected or if some generation is unexpectedly unavailable. The imperfect renewable forecasting can increase reserve requirements for the system. Regulation and load following reserves are those involved in flexibility. In power system operations with second or minute time resolution, regulation service provides the only response option for variations in the net load. On the time frame of economic dispatch, resources are balanced for load following in response to expected changes in the net load. In case of balance violations (imbalances), the system relys on regulation services to overcome real delivery time issue after the imbalance has caused frequency deviation or area control error (ACE), which is an undesirable outcome. In case of regulation service inadequacy, ramping capability shortage may result in leaning on interconnection. In

concept in operation and planning horizon of power system is needed. According to versatile impacts of flexibility issues in power systems, all possible flexibility types should be defined and classified. Due to the similar impacts of flexibility and reliability on power systems, the correlation between these two concepts needs to be clarified. Probabilistic flexibility metrics, both in operational and planning horizons of the power system, are required.

4. Flexibility impacts on power systems Understanding versatile impacts of flexibility on power systems can be practical in terms of finding both identification and treatment approaches to deal with rising flexibility issues. Accordingly, this section classifies and discusses the possible flexibility impacts. Chronologically classifying the flexibility effects leads to, shorttime, mid-time, and long-time categories as presented in Fig. 1. 4.1. Super short-term impacts Due to the near instant variations of VGs, some millisecond control systems are proposed in literature namely, low voltage ride-through, reactive power control, Supervisory Control and Data Acquisition (SCADA) information, voltage control, output

4.2. Short-term impacts

Fig. 1. Chronological classiﬁcation of ﬂexibility resources.

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frequent start-ups and reduced plant efficiency, as well as additional capital costs due to component replacement can also be expected. A recent research [18], however, states that increasing wear and tear costs and the corresponding emission impacts are negligible in compare with the displaced fuel costs that result from wind and solar. 4.4. Long-term impacts In the long-term, carbon restrictions will encourage investments' shift toward low-carbon base-load technologies such as nuclear, geothermal, and Carbon Capture and Sequestration (CCS). Many existing and proposed configurations for such facilities have limited operational flexibility due to high minimum output levels and limited ramping rates. This decrease in operational flexibility can be particularly problematic in combination with variable renewables-whose increased adoption is also encouraged by emission limits of all kinds [9]. Moreover, due to the low flexible characteristics of base-load units, these units might remain shut down. Consequently, the investment rate of return will be decreased and the base-load units will lose their investing attractiveness. In such a situation, fast response units will be more prevalent, but they generate electricity with higher prices. Fig. 2. Thermal power plant classification.

addition, when power balance is violated, the real time energy price is no more based on economic bids, but by administrative penalty prices, which may impact market efficiency in the long run [21,26]. It is worth noting that in addition to the conventional regulation and contingency reserves, flexibility reserves are recently proposed which respond to large VG resource fluctuations [26,30]. The important point is that wind ramps require the same types of responsive resources as conventional contingencies. Reserves may or may not be able to be shared between contingencies and wind ramps and this issue is still being investigated [36]. Apart from reserve increasing trend due to the increasing penetration rate of VGs, minimum output boundary creates another short-term operational problem. Suppose that nondispatchable power production, in highly VG penetrated networks, can meet all of the demand during some periods. It will cause base-load generating units, such as coal and nuclear plants, which can reduce their generation up to 90% of their outputs, to shut down. Then, suppose that just after variable generation output drops, in this case large price spikes will occur due to the unavailability of low cost base-load units. On the other hand, the large thermal mass of these units can require hours (combined cycle), days (large steam plants) or even weeks (nuclear) before the facility can again be restarted. Such cycling is also costly in terms of fuel, manpower, and increased maintenance [36,37]. The additional maintenance cost is discussed in the following section. 4.3. Mid-term impacts Large-scale deployment of variable renewable energy sources, particularly wind power, has led to increased plant cycling in power systems. Cycling may be defined as frequent start-ups or ramping of units. In base-load operation units, cycling can impose considerable levels of damage within the plant's components leading to increased maintenance requirements and forced outage rates. Cycling operation can cause thermal shock, metal fatigue, corrosion, erosion, and heat decay [38]. The wear-and-tear which arises due to cycling will incur increased maintenance costs for generators, and in addition to this, loss of revenue due to more frequent and longer outages, increased fuel costs due to more

5. Improving flexibility It is supposed that the main stream to prevent undesirable flexibility impacts on power systems is to identify and develop all available resources contributing in enhancing the overall flexibility of the power system. Hence, this section is dedicated to introduce power system flexibility resources. It is imaginable that flexibility can be provided from both supply and demand sides. According to Fig. 2, supply side can contribute in providing flexibility through three major sets of plants namely, peak plants, mid-merit plants, and baseload plants. Fig. 2 specifies the corresponding plants due to their ability to change their power output. Startup/Shut down, Ramp rates, and Minimum up/down times along with power generation ranges are comparing factors to classify the capability of plants in providing flexibility. It is worth noting that some nuclear plants can ramp around their power output. This capability may be limited in case of contingencies due to economic or security reasons [12,28]. Non-thermal power plants can also contribute in smoothing variable generation outputs. Hydropower plants are among the near instant response non-thermal units to maintain supply demand balance with an acceptable pace. A full reservoir hydropower can perform like a conventional thermal power plant. Hydropower plant, however, is a part of larger river system and, due to the cascading dams' effects, agricultural, and environmental reasons, it usually produces power under certain constraints. Thus, classification of hydropower plants is based on four following criteria namely, reservoir size, plant capacity, design flow, and head height. Apart from supply side alternatives to improve flexibility in power system, Demand Side Resources (DSR) along with storage facilities can properly mitigate the versatile rising issues in power systems with significant variable generation mixes. According to [39,40] storage functionalities can be categorized, based on their quickness to respond, into three major classes namely, fast response and mid response storage facilities along with electric vehicles, as also shown in Fig. 3. It can be mentioned that cycling efficiency, operation and maintenance (O&M) costs, and cost per MW are three major criteria for their comparison. According to the fact that displacing conventional generation with wind and solar; the amount of generation that is available to provide response will be reduced. Demand response may be able to fill this gap, benefiting renewables by facilitating their integration, the responsive loads, through paid services, and all customers through

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Fig. 3. Energy storage system classiﬁcation.

lower electricity bills [41]. The priority factors in relieving variable generation impacts are response rate, response period, response speed, and response magnitude. Demand side resources can be divided into two major categories due to their functionalities namely, Energy Efficiency (EE) and Demand Response (DR). Priceresponse mechanisms, shaving peaks, load shifting, and Time of Use tariffs were historic demand response programs that have focused on reducing overall electricity consumption by increasing economic efficiency [42]. More recent demand response programs, however, require responsive loads to provide contingency reserves and even minute-to-minute regulation. Instead of reducing overall power system stress by reducing peak loading over multiple hours, these programs are targeted to immediately respond to specific reliability events [43,44]. Accordingly, demand response programs can be classified into four groups namely, price-responsive demand, peak shaving, reliability response, and regulation response. The fact that variable generation sites are far from consumption areas may cause some congestion problems due to the capacity constraints of existing transmission lines, during some high generation occasions. Aggregating power systems, however, seems able to relieve such congestions. The grid interconnections strongly increase the idea of the worldwide concept of Smart Grids. Smart grid in brief is the use of sensors, communications, computational ability and control in some form to enhance the overall functionality of the electric power delivery system [27,44]. It is imaginable that due to the increasing informational transactions in power systems, modified transmission control systems such as temperature monitoring, wide area monitoring and protection systems, may not only intelligently increase the available line capacities, but also efficiently manage variable generation outputs throughout the power system. This is done through optimally deploying demand side management resources such as demand response, storage facilities, and electric vehicles. In addition, new transmission technologies can enhance transmission flexibility. Transmission lines can be rewired with hightemperature wires to increase power flow capacities, although high currents may worsen reactive power and voltage stabilities. Moreover, high voltage direct current transmission lines, Flexible Alternative Current Transmission systems (FACTS), and super conducting fault current limiters can enhance the overall controllability of AC transmission systems. The improved controllability can be extended by line temperature monitoring systems, Wide Area Monitoring Systems (WAMS) [27], and new protection schemes. Line temperature monitoring can indirectly increase flexibility of the transmission

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line to allow more power transmission during high wind potential. Output from wind and wave power generators increases with wind speed, which also cools transmission lines in the same locale, thus increasing their carrying capacity. This correlation can be used as the basis for a number of different methods for enhancing carrying capacity, based on measurement of changing line temperature, fluctuating weather conditions or increasing/decreasing line voltage (sag) [12]. Market procedures can also increase the overall flexibility called as virtual flexibility. Virtual flexibility relates to the mechanisms and procedure regulations to ease integrating high percentage of variable generations. Accordingly, the following mechanisms are presented in literature to increase the ability of power systems to cope with the variabilities derived from VGs. Buying additional regulation reserves can provide additional flexibility. In this regard, California Independent System Operator (CAISO) recently introduced new market-based products that allow for the identification, commoditization and compensation of the needed flexible capability. It is assumed that generating units can bid for 5-min ramp products which is limited to by how much a resource can ramp within 5 minutes. Continuity is the other special feature of the ramping products, while regulation and operating reserves are dispatched after major contingency occurs. In addition, day-ahead procured ramping products can be preserved to be deployed in real-time markets [34]. Reference [34], clarifies that flexible ramping products can be modeled in all three day-ahead markets, real time unit commitment, and real time dispatch procedures. The aim of modeling flexible ramping products in day-ahead markets is to make sure that unit commitment decisions for long start units, and flexible ramping capability awards are feasible in response to net load real-time uncertainties. The purpose of modeling flexible ramping in Real Time Unit Commitment (RTUC) is to make real-time unit commitment decisions, and to allocate flexible ramping headroom so that the system has sufficient flexible ramping capability. Therefore, it can be expected to have sufficient ramp capabilities during real operating time.

6. Flexibility barriers According to [18] there is no technical barrier to accommodate the integration of up to 35% wind and solar energy on a subregional basis if adequate transmission is available. It can be inferred that non-technical barriers such as lack of market regulations and bottlenecks in cross border interconnections are major shortcomings. Hence, there is a need to adhere inter-area scheduling protocols that are intended to provide certainty to other control areas so that their operations can be managed in an orderly and controllable manner [13].

7. Conclusion This paper presents a comprehensive literature review regarding recent progresses on emerging additional flexibility requirements in power systems due to significant uncertainty and variability from increasing penetration rate of non-dispatchable generating units. In this regard, the current paper provides a categorization of versatile temporal impacts of high integration of renewables along with a classification of resources with the capability of enhancing flexibility in future power systems. Based on the analysis and discussion of the existing research literature, various possibilities to extend relevant research is pointed out as follows. More efficient scheduling of the generating units requires new methods in power system operation in order to mitigate large

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fluctuations in renewable generation outputs while maintaining power system reliability. Although stochastic optimization has advantages in accounting for uncertainty and risks in many fields, complexity and computational requirements of such techniques remain barriers regarding their application in future operation of power systems. In contrast to the stochastic programming methods, robust optimization deals with uncertainty without relying on the information of underlying probability distributions and minimizes the worst-case cost regarding all realizations of the uncertain parameters. This approach, however, produces very conservative solutions, but it shows an acceptable tractability from a computational point of view. Accordingly, newer methods such as data-driven distributionally robust optimization [45], data-driven chance constrained optimization [46], decision-rule approximation methods [47,48] and other scenario reduction techniques should be investigated to overcome some challenging data issues in power systems [49]. The hourly generation scheduling is also challenged by high sub-hour net load variabilities. As a result, more detailed modeling with various temporal resolutions such as sub-hourly Security Constrained Economic Dispatch (SCED) and Security Constrained Unit Commitment (SCUC) with 15 min scheduling resolution will improve the model performance [50]. Among the variability impacts on short-term operation of power systems, ramp scarcity represents the major issue. Thus, definition, identification, and classification of ramping events may reduce continuous and frequent supply-demand imbalances. Moreover, some ISOs like California Independent System Operator (CAISO) [51] and Midcontinent Independent System Operator (MISO) [52] introduced some new flexibility-based markets to allocate proper amount of ramping reserve capacities to be deployed in upcoming intervals. These markets, however, allocate the additional ramping capacities (i.e. Flexible Ramping Product (FRP) in CAISO and Ramp Capability Product (RCP) in MISO) based on deterministic scheduling which ignores the uncertain inherent of net load deviations. Future researches are required to further investigate the probabilistic/stochastic ramping capability determination. Besides, it is also important to develop the right incentive structures able to ensure that units participating in flexibility markets have the proper incentives to follow these directions [53]. It is believed that simultaneous energy and ramping capability market clearing by using recently announced multi-stage optimization techniques can help power system operators to make near optimal decisions. In addition, since distinguishing between ramping capability, regulation and load following reserves is not always easy, some research streams should assess the prices, payments and optimally allocation of these ancillary services.

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