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Smart Grid - A Reliability Perspective
Khosrow Moslehi, Member, IEEE, Ranjit Kumar, Senior Member, IEEE
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increasing resiliency against component failures and natural disasters, and by eliminating or minimizing frequency and magnitude of power outages subject to regulatory policies, operating requirements, equipment limitations and customer preferences. Such control actions can be more responsive than human operator actions. Efficiency enhancement by maximizing ast utilization Resiliency against malicious attacks by virtue of better physical and IT curity protocols. Integration of renewable resources including solar, wind, and various types of energy storage. Such integration may occur at any location in the grid ranging from the retail consumer premis to centralized plants. This will help in addressing environmental concerns and offer a genuine path toward global sustainability by adopting “green” technologies including electric transportation. Real-time communication between the consumer and utility so that end-urs can actively participate and tailor their energy consumption bad on individual preferences (price, environmental concerns, etc.). Improved market efficiency through innovative solutions for product types (energy, ancillary rvices,
risks, etc.) available to market participants of all types and sizes. Higher quality of rvice – free of voltage sags and spikes as well as other disturbances and interruptions – to power an increasingly digital economy.
Abstract— Increasing complexity of power grids, growing demand, and requirement for greater grid reliability, curity and efficiency as well as environmental and energy sustainability concerns continue to highlight the need for a quantum leap in harnessing communication and information technologies. This leap toward a “smarter” grid is now widely referred to as “smart grid”. A framework for cohesive integration of the technologies facilitates convergence of acutely needed standards and protocols, and implementation of necessary analytical capabilities. The paper critically reviews the reliability impacts of major smart grid resources such as renewables, demand respon, storage. We obrve that an ideal mix of the resources leads to a flatter net demand that eventually accentuates reliability issues further. We then prent a gridwide IT architectural framework to meet the reliability challenges. This architecture supports a multitude of geographically and temporally coordinated hierarchical monitoring and control actions over time scales from milliconds and up. Index Terms— Smart grid, power grid, IT infrastructure, architecture, distributed intelligence, autonomous system, software agent, global coordination, temporal coordination, execution cycle, fas
t local control, real-time, large-scale system, distributed system, power system operation, power system control, coordinated operation, power system curity, power system reliability, lf-healing grid.
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HE utility industry has been utilizing advances in communication and information technology over the years in order to improve efficiency, reliability, curity and quality of rvice. Increasing complexity in managing the bulk power grid, growing concerns for environment, energy sustainability and independence, aging ast ba, demand growth and quest for rvice quality continue to accentuate the need for a quantum leap in application of such technologies. This leap toward a “smarter” grid is now widely referred to as “smart grid”. Smart grid (SG) is envisioned to take advantage of all available modern technologies in transforming the current grid to one that functions more intelligently to facilitate: • Better situational awareness and operator assistance. • Autonomous control actions to enhance reliability by
Khosrow Moslehi is with ABB Network Management, Santa Clara, CA (Khosrow.Moslehi@) Ranjit Kumar is a consultant to ABB Network Management Paper 10SG0068, IEEE PES Conference on “Innovative Smart Grid Technologies” January 19-20, 2010, Washington, DC.
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I. INTRODUCTION •
杏花The momentum for the “Smart Grid” vision has incread recently due to policy and regulatory initiatives, as exemplified by [1,2,3,4]. Numerous and diver stakeholders are striving to realize the above smart grid goals by advancing and deploying various technologies. The efforts can be categorized into the following trends: • Reliability • Renewable Resources • Demand respon • Electric storage • Electric transportation The above trends are also recognized as priority functional areas in the “FERC Smart Grid Policy Statement” [1]. Among the trends, system reliability has always been a major focus area for the design and operation of modern grids. The other trends involve distinct smart grid resource types with diver
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impacts on reliability. Renewable resources, while supplementing the generation capability of the grid and addressing some environmental concerns, aggravate the reliability due to their volatility. Demand respon and electric storage resources are necessary for addressing economics of the grid and are perceived to support grid reliability through mitigating peak demand and load variability. Electric transportation resources are deemed helpful to meeting environmental targets and can be ud to mitigate load variability. Balancing the diversity of the characteristics of the resource types prents challenges in maintaining grid reliability. Meeting the reliability challenges while effectively integrating the above resources requires a quantum leap in harnessing communication and information technologies. A common vision for cohesive integration of the technologies facilitates the convergence of standards and protocols that are so acutely needed and expedites the deployment of the technologies. Such common vision can be arrived through a systematic approach bad on understanding of reliability challenges in modern power grid as well as fundamental impacts of integrating the evolving smart grid resource mix. This paper first provides an overview of the grid reliability challenges and then prents a critical review of the salient reliability impacts of the four smart grid resource types identified above. We obrve that an ideal mix of the resources that flattens net demand would eventually accentuate reliability issues even further. Meeting reliability challenges requires a grid-wide IT infrastructure that provides coordinated monitoring and c
ontrol of the grid. We then prent an architectural framework for such IT infrastructure. The architecture is designed to support a multitude of geographically and temporally coordinated hierarchical monitoring and control actions over time scales ranging from milliconds to operational planning horizon. Such capability is necessary to take full advantage of the modern measurement technologies (e.g. PMUs) and control devices (e.g. FACTS). The architecture is intended to rve as a concrete reprentation of a common vision that facilitates the design and development of various components of the IT infrastructure and emergence of standards and protocols needed for a smart grid. II. RELIABILITY CHALLENGES Reliability has always been in the forefront of power grid design and operation due to the cost of outages to customers. In the US, the annual cost of outages in 2002 is estimated to be in the order of $79B [5] which equals to about a third of the total electricity retail revenue of $249B [6]. A similar estimate bad on 2008 retail revenue would be of the order of $109B. Much higher estimates have been reported by others. The reliability issues in modern power grids are becoming increasingly more challenging. Factors contributing to the
challenges include: • Aggravated grid congestion, driven by uncertainty, diversity and distribution of energy supplies due to environmental and sustainability concerns. The power flow patterns in real-time can be significantly different from tho considered in the design or off-line analys. • More nu
merous, larger transfers over longer distances increasing volatility and reducing reliability margins. This phenomenon is aggravated by energy markets. • The grid being operated at its “edge” in more locations and more often becau of: o “Insufficient” investment and limited rights of way o Increasing energy consumption and peak demand creating contention for limited transfer capability o Aging infrastructure o Maximizing ast utilization driven by modern tools for monitoring, analyzing and control • Consolidation of operating entities giving ri to a larger “foot print” with more complex problems and requiring smaller error margins and shorter decision times. This problem may be aggravated by depletion of experienced personnel due to retirement, etc. Massive utilization of distributed resources tends to blur the distinction between transmission and distribution, and to accentuate the complexity and volatility of grid operation. III. RELIABILITY IMPACTS OF MAJOR SG RESOURCE TYPES The reliability impacts of the major smart grid resource types cited above are discusd below. Renewable Resources: Most rapidly expanding renewable resources are expected to be wind and solar. In the U.S., wind is expected to grow from 31TWh in 2008 (1.3% of total supply) to 1160TWh by 2030 (wind energy target of 20% of total supply of 5,800 TWh) [7]. The unpredictability of wind energy resources is indicated by their low capacity factors (typically 20 to 40% [8]) which are much lower than conventional generators. Fig. 1 shows variability of a typical CAISO wind resource.
Fig. 1: Example - Variability of Wind Resource Output This creates challenging problems in the control and
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reliability of the power grid. As can be en from Fig. 2, the variability of wind energy has little correlation to the variability of the load and hence contributes only a little towards meeting ERCOT’s peak load. This is despite expected 18 GW of wind capacity.
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ca of wind Relatively high forecast errors especially for longer horizons Congestion issues at transmission level due to large installations and at distribution level due to disperd resources. Operational performance issues such as voltage and regulation
Conventionally, hydro, pumped storage and gas turbines have been ud as a remedy to address the variability of the net demand. As renewables grow over the long run, incread penetration of demand respon, storage devices and utilization of plug-in electric vehicles (PEVs) will complement
the conventional remedies. Demand Respon / Load Management Fig. 2: Example - Impact of 18 GW of Wind Power Capacity The variability of wind power is impacted by the design of the equipment as well as their geographical distribution. Large scale wind resources are typically far away from loads and conquently face various transmission limitations including thermal, voltage and stability issues. The wind power forecasting errors also prent scheduling problems. The forecasting errors could be in excess of 25% depending on the terrain, forecast horizon and forecasting methodology [9]. Wind generators also prent problems regarding low voltage ride through (LVRT). Wind power variability has a relatively small adver impact on regulation requirements [10]. Solar is the most abundant source of energy. The annual solar energy reaching the surface of the earth is about 1,000 times the current world-wide fossil fuel consumption in a year [11]. Cumulative installed solar capacity is expected to reach 16GW by 2020 [12]. The two prevailing technologies to harness this energy are photovoltaic and thermal. The variability of solar energy resources is very much impacted by climate and sunlight availability. The capacity factors for photovoltaic are typically 10 to 20%. For solar thermal plants this may reach over 70% with storage [13]. Large scale solar resources could be far away from loads and conquently face various transmission limitations. On the other hand, solar resources have a positive correlation with air conditioning load demand in warmer climates. Renewable resources generally have adver impact
on grid reliability due to the following factors: • Variability and low capacity factors making the net demand profile steeper (as depicted in Fig. 2) • Low correlation with the load profile especially in the Demand respon allows consumer load reduction in respon to emergency and high-price conditions on the electricity grid. Such conditions are more prevalent during peak load or congested operation. Non-emergency demand respon in the range of 5 to 15% of system peak load can provide substantial benefits in reducing the need for additional resources and lowering real-time electricity prices [14]. Demand respon does not substantially change the total energy consumption since a large fraction of the energy saved during the load curtailment period is consumed at a more opportune time – thus a flatter load. Load rejection as an emergency resource to protect the grid from disruption is well understood and is implemented to operate either by system operator command or through underfrequency and/or under-voltage relays. In a smart grid, the load rejection schemes can be enhanced to act more intelligently and bad on customer participation. Price bad demand respon/load management as a system resource to balance demand and supply has not been widely adopted yet. Contract bad participation has been typically below 5% of peak load [14]. In a smart grid, real-time price information enables wider voluntary participation by consumers. Demand respon can be implemented through either automatic or manual respon to price signals, or through a bidding process bad on direct communications between the consumer
and the market/system operator or through intermediaries such as aggregators or local utilities (Fig. 3). In addition to capability to flatten the load profile, demand respon can rve as an ancillary resource. As such, demand respon schemes could improve reliability.
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IV. ULTIMATE RELIABILITY IMPACT OF SG RESOURCES As depicted in Fig. 4, under ideal conditions, demand respon, storage and electric vehicles will be cloly coordinated with all other resources such that the net load profile would be nearly flat. This implies that the grid would be operated clor to near-peak load conditions almost all of the time. Initially, flattening of the net load profile tends to improve reliability by decreasing the peak. However, over time, as the load grows, forces of optimal transmission and distribution ast utilization will push the net load clor to the system operating limits. Thus, the system will be clor to its “edge” more often, leading to higher susceptibility to failure and to accentuation of the reliability issues; hence, the need for a “smart grid” solution.
Fig. 3: Communications for Demand Respon Storage Devices Most of the existing storage resources are hydro and pumped storage. However, growth potential for the resources is much s
maller than the need for storage necessary to counter growing net demand variability prented by new wind and solar resources. Various storage technologies are emerging to fill the gap. Battery storage appears to be most promising due to improvements in technology as well as economies of scale. Storage resources tend to make the net demand profile flatter and, as such, are expected to improve reliability. In addition, most battery storage devices can respond in subcond time scales. Hence they can become valuable enablers of fast controls in a smart grid. Storage resources of various sizes can be distributed throughout the grid ranging from endu loads to major substations and central power stations. This feature can help to alleviate congestion at both transmission and distribution levels. Electric Transportation Plug-in electric vehicles (PEV, eCAR, etc.) continue to become more popular as environmental concerns increa. They are a significant means to reduce green hou gas (GHG) and reliance on fossil fuels. They will be a significant factor in load growth with a potential to eventually consume 600TWh/year assuming 30kwh for a 100-mile trip [15], and 10,000 miles per year for 200 million vehicles in the U.S. For greater adoption of all-electric vehicles, the issue of recharge time has to be resolved. Long recharge times lead to generally unacceptable level of vehicle unavailability and short recharge times have potential to increa congestion, especially at the distribution levels. From purely reliability viewpoint, electric transportation has features similar to both demand respon resources and storage resources. As PEVs prent a signi
ficant factor of load growth, this can also aggravate the demand variability and associated reliability problems depending on the charging schemes and consumer behavioral patterns.
Fig. 4: Ultimate Reliability Impact of SG Resources V. IT INFRASTRUCTURE FOR SMART GRID Realization of the smart grid vision requires meeting the ever increasing reliability challenges by harnessing modern communication and information technologies to enable an IT infrastructure that provides grid-wide coordinated monitoring and control capabilities. Such IT infrastructure should be capable of providing fail proof and nearly instantaneous bidirectional communications among all devices ranging from individual loads to the grid-wide control centers including all important equipment at the distribution and transmission levels. This involves processing vast number of data transactions for analysis and automation. This requires a high performance infrastructure capable of providing fast intelligent local sub-cond respons coordinated with a higher level global analysis in order to prevent or contain rapidly evolving adver events. Centralized systems are too slow for this purpo. A distributed architectural framework can enable the high performance infrastructure with local intelligent sub-cond respon using modern technologies bad on: • Better telemetry: utilizing PMU technology for faster, time-stamped, higher accuracy, sub-cond scanning to enable timely grid-wide situational awareness. • Faster control devices: bad on power electronics to enabl
e fast automated control actions, for voltage and power flow management at both transmission and distribution levels.
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More robust controls: proactive and adaptive adjustment of protection and control ttings for widearea monitoring and controls including intentional islanding (beyond currently employed ad-hoc schemes). Embedded intelligent devices (IEDs): to enable adaptive and intelligent control for implementing: o Equipment level fault diagnosis and bad data identification o Operation within the constraints remotely prescribed by system operators or control centers o “Intelligent” RAS/SPS, etc.
o Autonomous restoration of equipment o Autonomous local control actions Integrated and cure communications: highly distributed and pervasive communications bad on open standards to allow for flexible network configurability to assure fail-proof monitoring and automation and bidirectional communications between all operators and agents. Enhanced computing capabilities: fail-proof and cure systems for reliable analys to support operator decisions and autonomous intelligent functional agents orchestrated throughout a geographically and temporally coordinated hierarchy in the grid-wide IT infrastructure [16]. Internet technology: internet protocols to facilitate data exchange, process control and cyber curity to implement a standards-bad distributed architecture with open interfaces. Plug-and-play hardware and software components in a rvice oriented architecture bad on standards and technologies such as messageoriented middleware and web rvices to enable amless integration of intelligence throughout the IT infrastructure ranging from equipment level IEDs to all higher levels.
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tasks in the areas of: • • • • Data Acquisition and Model Management System Monitoring (e.g., state estimation, curity analys, look-ahead/forecasting) Performance Enhancement (e.g., efficiency enhancement, corrective/preventive actions, curity constrained dispatch) Control (e.g., AGC, automatic emergency controls, special protection schemes)
Fig. 5: Hierarchical Architecture for Smart Grid The functional tasks potentially apply to every level from customer resource, feeder, and substation to the entire grid (e.g., a substation may perform its own share of state estimation instead of just providing raw data). The agents facilitate more ubiquitous u of local controls coordinated by global analysis, real-time tuning of control parameters, automatic arming and disarming of control actions in realtime, as well as functional coordination in the hierarchy, and in multiple timescales. The virtual architecture allows amless integration of intelligence at all levels so that the locations of specific rvices and data are virtualized and transparent throughout the infrastructure subject to cyber curity. Such modular, flexible and scalable infrastructure meets the global operational needs and allows for evolutionary implementation on a continental scale. It can respond to actual steady-state and transient operating conditions in real-time more effectively than conventional solutions that depend on off-line analys. The agents operate at different timescales ranging from milliconds to hours corresponding to the physical phenomena of the power grid. Their actions are organized by execution cycles. An execution cycle refers to a t of related
Architecture A systematic "operations driven" approach as oppod to an ad hoc "methods driven” approach is adopted for developing the architectural framework propod above. This approach is b
ad on consideration of all key operating concerns in categories such as performance enhancement, equipment limits, operating limits, system protection, and rapid recovery. The resulting architecture calls for distribution and coordination of the necessary functional tasks in a virtual hierarchy in three dimensions (Fig. 5): • Organizational/Control (grid, region, control area, zone/vicinity, substation, feeder, customer, etc.) reprenting operational responsibilities • Geographical area (Region 1…j, Substation 1…n, etc.) • Functions (forecasting, alarming, voltage control, etc.) Autonomous intelligent agents are deployed, as needed, on a grid-wide computing network throughout the infrastructure to provide rvices necessary for the execution of functional
