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Systems and Control Opportunities in the Integration of Renewable Energy into the Smart Grid ? E. Bitar ∗ P. P. Khargonekar ∗∗ K. Poolla ∗∗∗ ∗ E. Bitar is with the Department of Mechanical Engineering, U.C. Berkeley [email protected] (corresponding author) ∗∗ P. P. Khargonekar is with the Department of Electrical and Computer Engineering, University of Florida [email protected] ∗∗∗ K. Poolla is with the Department of Electrical Engineering and Computer Science, U.C. Berkeley [email protected] Abstract: The Smart Grid is among the most important and ambitious endeavors of our time. Deep integration of renewable energy sources is one component of the Smart Grid vision. A fundamental difficulty here is that renewable energy sources are highly variable – they are not dispatchable, are intermittent, and uncertain. The electricity grid must absorb this variability through a portfolio of solutions. These include aggregation of variable generation, curtailment, operating reserves, storage technologies, local generation, and distributed demand response. The various elements in this portfolio must be dynamically coordinated based on available information within the framework of electricity grid operations. This, in turn, will require critical technologies and methods drawn from optimization, modeling, and control, which are the core competencies of Systems and Control. This paper catalogues some of these systems and control research opportunities that arise in the deep integration of renewable energy sources. Keywords: Smart Grid, Renewable Energy Integration, Uncertainty, Systems, Control, Modeling 1. THE OPPORTUNITY What is the Smart Grid? The Smart Grid is a vision of the future of the electric energy system. There are many visions of this future, but perhaps these are best summarized by US Energy Secretary Steven Chu who writes that “the Smart Grid is the key enabler for: integration of renewable energy sources into the grid, management and deployment of energy storage, load management, system transparency, and cyber and physical security of the electric energy system”. Concerns over global climate change and carbon emissions have motivated development of integration of renewable energy generation from sources such as wind and solar to meet our electric energy needs. Another particularly compelling motivation behind the Smart Grid initiative is that the use of advanced communication, information, and control technologies may reduce the need for expensive investments in the physical infrastructure – generation, transmission, and distribution. Realizing these visions of the Smart Grid and its benefits for combating global climate change will require a portfolio of solutions such as deep integration of renewable resources, grid scale storage technologies, incorporation of micro-generation, demand response and consumer participation, new market ? Supported in part by OOF991-KAUST US LIMITED under award number 025478, the UC Discovery Grant ele07-10283 under the IMPACT program, and NSF under Grant EECS-0925337, and the Florida Energy Systems Consortium. instruments, advances in materials, sensors, and powerelectronics, dedicated communication networks with secure protocols, and targeted infrastructure investments. Renewable Energy Integration The scope of Smart Grid objectives and technologies delineated above is indeed daunting. In this paper we will confine ourselves to one of the many components of the Smart Grid vision: deep integration of renewable energy into the electric grid. Renewable energy sources such as wind and solar are fundamentally different from conventional generation such as coal, nuclear, natural gas. The energy production from these renewable sources is not dispatchable [cannot be controlled on demand], intermittent [exhibits large fluctuations], and uncertain [random or not known in advance]. We will use the term variability to encompass these three characteristics of renewable generation [33]. Variability is the most important obstacle in integrating renewable generation into the electric energy system at deep penetration levels. The Variable Generation Problem For solar power, there is the natural diurnal cycle of variability in insolation [incident solar radiation]. Beyond this, cloud-cover can cause significant ramps in solar insolation and electric power output. Solar insolation can change by more than 80% of the peak insolation in a matter of seconds [31]. The corresponding ramp in the solar power plant output is also very large but it also depends on panel size and geographical spread. In addition, inverter tripping adds to the variability in solar plant output. Wind speed and direction experience significant variations at multiple time scales: minutes, hours, daily, seasonally, and annually. In addition to the slow and gradual variations, wind plant output can ramp up or down in minutes [23]. For example, on December 29-30, 2008, wind power output experienced 95% drop in a two hour period in the BPA balancing authority [35]. Also, severe weather events can cause wind speed to become dangerously high making operation of wind turbines unsafe, forcing shutdown. Aggregation over large areas tends to reduce the variability in wind power production. These difficulties are compounded by the fact that it is difficult to obtain reliable and accurate forecasts of wind power production. Forecasting difficulty increases with forecast horizon. Dealing with Variable Generation As we consider the variable generation characteristics of renewable energy sources, it should be noted that electric power system operations are designed to accommodate the natural (and very large) variability in load demand as well as planned and unplanned contingencies. This is done at different time-scales through load-frequency control, operational reserves, scheduling and unit-commitment, demand response, and load shedding. At deep penetration levels, renewable generation add significantly to the extent of the overall variability that must be handled. It has been suggested that existing mechanisms to handle demand fluctuations are adequate for renewable generation variability up to 20% penetration levels [15, 50]. Reconfiguring these existing mechanisms to handle the increased variability comes with significant cost issues and operational challenges [12]. Systems and Control Opportunities At deep penetration levels, dealing with variable generation requires a portfolio of technologies which, in turn, introduce operational challenges for the electricity grid. Improved forecasting of wind and solar energy at multiple time-scales reduces uncertainty and permits efficient scheduling of these variable resources into the generation mix. Dynamic optimization through optimal resource scheduling and control can reduce the need for various types of operational reserves thereby reducing integration cost and increasing the carbon reduction benefits of renewable generation. Optimal operation of grid scale storage can reduce the curtailment of wind energy when the production of wind energy is too high. Smart charging of plug-in electric vehicles can make greater use of wind energy at night when the industrial and residential demand is low. Demand response through real-time pricing or appropriate contracting mechanisms can reduce the need for peak generation and improve matching of renewable generation with load. Managing increased variability in the electricity grid will need advanced large scale distributed control and optimization algorithms. Each of these avenues to address the fundamental challenge of renewable resource variability requires systems and control technologies. We submit that system identification, modeling, estimation, distributed optimization, and optimal control will play central roles here. What is in this paper? In this paper we will explore those aspects of variable generation integration where systems and control technologies will play a critical role in obtaining effective solutions. Our objective is to catalogue an incomplete list of such problems and analyze the underlying issues. The opportunities abound for our community of researchers, and the time is ripe to seize them. 2. SYSTEMS AND CONTROL OPPORTUNITIES We now discuss a number of specific problems which need to be solved in order to achieve deep penetration of renewable energy sources where systems and control technologies can make critically important contributions. A. Forecasting Challenges in electric grid operations with deep renewable penetration become much more tractable if accurate forecasts of wind and solar energy production are available in advance. Accurate forecasts can alleviate negative impacts on the required spinning reserves for reliable operation of the grid. They reduce the total cost of integration of renewable energy into the grid. Wind power forecasting is a very active area of research [see [8] for a recent survey and references to the literature]. Wind power is a nonstationary stochastic process. There are two main approaches to this problem: (a) physical approach where numerical weather predictions are coupled with a collection of wind turbine physical models and data to predict the wind power output of the wind farm, and (b) techniques that combine empirical time-series models such as ARMA, statistical forecasting, pattern matching, neural networks, fuzzy systems, etc. with numerical weather predictions to forecast the wind farm power output. Commercial wind power forecasting tools include combinations of these approaches as well. Spatial diversity can decrease the total uncertainty in the output of a wind farm. Moreover, the forecasting error generally decreases with forecast horizon. At short time-scales [minutes to hours], time-series models are among the best performers. State-of-the-art tools provide error rates of 4-6% for short term forecasts. For longer forecast horizons [24-48 hours], the prediction errors are large and can exceed 20-30%. A special issue in wind forecasting is the occurrence of wind ramps. In addition to point forecasts, newer approaches to dealing with wind uncertainty require forecasts of probability distribution functions. Similar comments apply to forecasting methods for solar energy production. Satellite and ground-based sky imaging tools can be used to monitor and forecast cloud movements. Renewable generation forecasting is clearly an area where advanced techniques from linear and nonlinear estimation and prediction can have a major impact. B. Electricity Markets Electricity markets are recognized to be fundamentally different than commodity markets. Electricity has physical constraints on transmission, generation, scheduling, and storage, economic constraints on generation costs and system infrastructure, and quality of service constraints demanded by consumers. As a result, traditional com- modity market structures are not suitable for electricity. A certain level of centralized coordination and control is necessary to ensure safe, economic, and reliable operation of the electric power system. While there is increasing competition and decentralization in generation and retail distribution, transmission and system operation remain centralized. In its most deregulated form, the system is managed by an independent system operator [ISO] or a transmission system operator [TSO]. A common market structure [42, 30, 32, 25] consists of two successive ex-ante markets: a day-ahead (DA) forward market and a realtime (RT) spot market. The DA market permits qualified generators to bid and schedule energy transactions for the following day. Depending on the region, the DA market closes for bids and schedules by 10 A.M. and clears by 1 P.M. on the day prior to the operating day. The schedules cleared in the DA market are financially binding and are subject to deviation penalties. As the schedules submitted to the DA market are cleared well in advance of the operating day, a RT spot market is employed to ensure the balance of supply and demand in real-time by allowing market participants to adjust their DA schedules based on more accurate wind and load forecasts. The RT market is cleared 5 to 15 minutes before the operating interval, which is on the order of 5 minutes. In the context of deep penetration of renewable energy, many new questions arise regarding these market mechanisms and structures [21]. Renewable energy producers are often given favorable treatment via public policy support through subsidies, price guarantees or other extra-market mechanisms. A major unresolved issue in this context is the pricing of carbon emissions by certain types of energy producers. Also, costs of integrating variable generation are often socialized and distributed to other participants in the energy markets. However, as the penetration level increases, such mechanisms are likely to be untenable and the renewable energy producers will have to operate in the market along with traditional energy producers. What new market mechanisms are necessary to enable deep penetration of renewable energy generators? Since renewable generation is inherently variable and uncertain, it might become necessary or advantageous to devise novel contract structures [ex: interruptible contacts [49]] that explicitly deal with this variability. Also, more frequent intra-day markets where the wind and solar energy producers can enter bidding closer to the delivery time will be better matched to the uncertainty in their production. These recourse markets may also help in reducing the cost of integrating variable generation by separating load following reserves from regulation reserves and/or reduce the need for more expensive regulation reserves. Fast intraday markets may be regarded as a form of closed loop control to deal with variabile generation. Designing and optimizing new market mechanisms that explicitly account for variable generation is an important research direction that will involve diverse methods from control theory and stochastic dynamic programming. C. Joint Optimization of Wind and Storage At deep penetration levels, energy storage is expected to be a key enabling technology for the Smart Grid [13]. Energy can be stored using pumped hydro, compressed air, batteries, flywheels, molten salt, etc. Today, pumped hydro is the most widely used storage modality but it is geographically limited. In the future, electric vehicles may provide distributed energy storage capacity which could be harnessed with Smart Grid technologies. Depending on the capacity of the storage device, it can be used on the generation and transmission side or on the distribution side. From the perspective of renewable integration, the key consideration is – when should energy storage be distributed across the grid versus co-located at generation sites. These are dramatically different visions of large scale storage deployment. If storage devices are co-located with the renewable generation plant, it opens up the possibility of real-time control of storage device to reduce the variability of the renewable energy output. This can be posed as a stochastic optimal control problem to match a given profile of power output by suitably extracting from and injecting into the storage device. An important question then is the minimum storage size needed to achieve a given level of probability of meeting the output power profile. The ultimate objective would be to make the wind plant dispatchable. This will require high precision control of the power output from the renewable plant. On the other hand, since storage is expensive [≈ $ 1.5M per MW-hr], it is quite possible that the best economic value can be derived by optimizing its operation at the grid level. On the other hand, in the scenarios involving distributed renewable energy generation, community scale distributed storage may be the optimal choice. Optimal sizing and operations of distributed storage modalities with different dynamic characteristics will require methods from distributed optimization and stochastic decentralized optimal control. D. Reserves and Local Generation Wind farm energy production variability can be compensated by the use of local generation sources such as natural gas or diesel generators. Local generation adds to capacity costs as well as operational costs of the system. Also, as natural gas plants produce carbon emissions, this approach attenuates the clean energy benefit from wind power production. One can pose a joint stochastic optimal control problem involving wind and local generation to deliver a certain amount of total power in the presence of stochastic wind variability. Deep penetration of renewables can have a very large impact on various different types of reserves needed to operate the grid and meet the relevant reliability criteria. Generally speaking, renewable generation has no significant impact on the contingency reserves as the largest contingencies arise from base-load generating units. By contrast, the impact on operating reserves and regulation reserves is substantial. There are many studies to assess the additional reserve requirements [15, 50]. Increased need for operating and regulation reserves is one of the pressing issues in integrating large amounts of renewable energy [9]. They add to the cost of integration and thereby reduce the beneficial impact of renewables. Since reserve generators often use hydrocarbon fuels, they detract from the positive impact of renewables on global warming. However, a critical assumption made in many of the current studies on required reserve requirements is that they assume no change in the current operating procedure. Hence, it may be possible to apply techniques from systems and control to mitigate the impact of variability in renewables on reserve requirements. For example, dynamic stochastic optimization of reserves that can meet the required loss of load probability by taking advantage of reduced uncertainty with shorter forecast horizons. Progress in both these research areas will use tools from stochastic optimization. E. Power System Stability Experience in island power systems indicates that severe system level problems arise when the penetration of wind power exceeds 15%. In Denmark, where the wind penetration exceeds 20%, this problem has been avoided by the use of interconnections to power systems in Sweden, Norway, Germany, etc. Spain has almost 100% reserve capacity. Analysis of the impact of high penetration of variable generation sources on the stability and performance [voltage, frequency, etc.] of the power system is a difficult problem. Transient frequency response, regulation response, automatic generation control, load following, and unit commitment processes, operating at different time scales [seconds to hours], together determine the overall impact on the voltage and frequency of fluctuations in the renewable power production. Also, wind turbines are variable speed generators as opposed to synchronous generators for traditional power generation. These variable speed generators [ex: doubly fed induction generator] may adversely impact the transient stability of the power system [3] as they reduce the inertia of the system by displacing synchronous generators. Analysis and design of suitable control systems to enhance power system stability at high penetration levels is a key problem where systems and control techniques will prove invaluable. Power system stability is a networked control problem, the scale of which will require the creative development of computational tools for stability analysis. F. Voltage Support Maintaining tight voltage control across the distribution system is an important part of delivering power quality. Tight voltage control is necessary for efficiency in many end-use applications. Distribution system voltage levels are traditionally controlled by adjusting load tap changes in substation transformers, and through capacitor banks to provide reactive power support locally. Integration of large amounts of renewable power in the distribution system [ex: rooftop solar] will create new sources of variation in the voltage level in the distribution system. The random locations of these generation devices can also lead to increased imbalance operation in the 3-phase distribution network. These variations and imbalances change along the length of the feeder circuit. There is active discussion of modifications to the standards (IEEE 1547-1547.8) which regulate connection of renewable generators to the distribution system. There are also considerable variations in local rules that also constrain the connection of renewable generators to the distribution system. Among other issues, there is concern that unless these regulations are properly conceived, they may lead to increased occurrences of disconnection from the grid due to voltage fluctuations [22]. Modern commercially available solid state inverters are capable of supplying (capacitive as well as inductive) reactive power. At the same time, fast communications of voltage measurements across the distribution network may allow for coordinated control actions. Thus, it is possible to conceive of control system architectures and algorithms that leverage these actuation capabilities to minimize voltage fluctuations [34, 39]. In addition, DSTATCOMs (Distributed Static Compensators) are controllable reactive power resources that can be employed to deal with voltage quality issues. What is the optimal placement of these devices? How should they be controlled to optimize metric of voltage quality? These are some of the emerging problems that are beginning to attract attention [41]. Mitigating undesirable voltage fluctuations will require the use of techniques from large scale distributed control and optimization. G. Matching Variable Generation to Adjustable Demand Demand side actions offer another strategy to absorb injected variability from renewable generation. There is a considerable effort in developing demand response as a Smart Grid technology for this (and other) objective. See for example [7, 29, 44]. Profitable business models for demand response with commercial customers (ex: refrigerated warehouses) have been realized by companies such as EnerNOC Corporation [14]. Much of the existing research studys implementation aspects (ex: communication infrastructure, appliance control, price signalling) of demand response. The operational aspects of matching variable generation with adjustable demand in transmission constrained power systems remain largely unexplored. There are a diversity of consumers with different electricity needs. Some of these are power consumers: they require power on demand and offer little opportunity for demand shaping [ex: continuous manufacturing facilities that cannot defer their demand for electricity]. Others are energy consumers: they can accept variable power within certain constraints with advance notice and coordination. Examples here include warehouses which can chill their refrigeration space on variable cooling schedules, and electric vehicle charging stations which can use variable charging schedules. What are generic models for the electricity needs of energy consumers? What is the set of power profiles P that they can accept for the period [0, T ]? Once we have models of acceptable power profiles from certain classes of energy consumers, we can formulate various flavors of demand matching problems. Consider a variable generator and an energy consumer [EC] who can absorb arbitrary power variability and assume we have no transmission constraints. In this happy situation, all variable generation can be sold to the EC, through bilateral contracts or other arrangements. However, if the transmission network is constrained, or if the energy consumer can accept only certain power profiles, then some variable generation must be curtailed and/or absorbed by operating reserves. How much variable generation can be absorbed by adjustable loads? This problem is compounded by the fact that the wind w(t) is random. The ideas above suggest introduction of the concept of Quality-of-Service [QoS] for power. On the generation side, QoS is intended to capture reliability in supply, while on the demand side, QoS is meant to capture variability in acceptable power profiles. Pricing mechanisms can be introduced to reflect QoS in both generation and demand, and may prove to be the natural mechanism to treat variability in competitive markets [46]. Matching variable generation to distributed demand response resources will require techniques drawn from model predictive control, and stochastic dynamic programming. phase measurement units [synchro-phasors], control of asynchronous wind turbines, power electronics for optimal operation of large PV arrays, reactive power compensation issues in wind farms, micro-grid control, distributed energy generation, etc. Even in the confines of our narrow focus, it is evident that our community of researchers in systems and control will be major contributors to realizing the vision of the Smart Grid. REFERENCES [1] H. Optimal Coordination Today, wind power is commonly treated as a negative load and the utility (or system operator) must accept all wind power produced at a fixed price (feed-in tariff). This forces the utility to use reserves to compensate for the injected variability in the wind power w(t). Reserve generation imposes both a capacity (reservation) and energy (use) cost. There are many different types of reserves, each with its own start, stop and ramping characteristics. Reserve capacity is traditionally procured well in advance (∼ 1 hr) of the delivery time. Greater forecast uncertainty therefore increases reserve capacity costs. This can be mitigated through: (a) improved forecasting, (b) reserve capacity purchases over short horizons, and (c) matching adjustable demand to variable generation [see subsection G above]. These strategies require coordination: optimal reserve power purchases and load adjustment strategies are dynamically coupled through forecast information. How can we dynamically aggregate variable generation with various types of reserves so that the portfolio collectively behaves as reliably as dispatchable thermal generation. In this dynamic portfolio problem we must select the most economical mix based on current generation forecasts and current cost information of available firming resources. Implementation of dynamic portfolios will leverage the Smart Grid infrastructure – sensors and communications to provide the necessary real-time information. The problem of optimally computing resource acquisition and dispatch decisions is a challenging constrained stochastic optimal control problem. [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] 3. CONCLUSIONS The deep integration of renewable energy resources into the electricity grid is a compelling and challenging endeavor. It offers a rich set of research problems, many of which will critically require systems and control methods in their solution. We have collated a partial list of specific problems driven by the variability and uncertainty of renewable resources. Our focus in this paper has been on the issue of integration of variable generation. We have not discussed many other research opportunities for the systems and control research community in the broad topic of Smart Grid. 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