Showing papers by "Pierluigi Mancarella published in 2016"

Boosting the Power Grid Resilience to Extreme Weather Events Using Defensive Islanding

Abstract: Several catastrophic experiences of extreme weather events show that boosting the power grid resilience is becoming increasingly critical. This paper discusses a unified resilience evaluation and operational enhancement approach, which includes a procedure for assessing the impact of severe weather on power systems and a novel risk-based defensive islanding algorithm. This adaptive islanding algorithm aims to mitigate the cascading effects that may occur during weather emergencies. This goes beyond the infrastructure-based measures that are traditionally used as a defense to severe weather. The resilience assessment procedure relies on the concept of fragility curves, which express the weather-dependent failure probabilities of the components. A severity risk index is used to determine the application of defensive islanding, which considers the current network topology and the branches that are at higher risk of tripping due to the weather event. This preventive measure boosts the system resilience by splitting the network into stable and self-adequate islands in order to isolate the components with higher failure probability, whose tripping would trigger cascading events. The proposed approach is illustrated using a simplified version of the Great Britain transmission network, with focus on assessing and improving its resilience to severe windstorms.

Integrated Electrical and Gas Network Flexibility Assessment in Low-Carbon Multi-Energy Systems

Abstract: In power systems with more and more variable renewable sources, gas generation is playing an increasingly prominent role in providing short-term flexibility to meet net-load requirements. The flexibility provided by the gas turbines in turn relies on the flexibility of the gas network. While there are several discussions on the ability of the gas network in providing this operational flexibility, this has not been clearly modeled or quantified. In addition, the gas network may also be responsible for supplying heating technologies, and low-carbon scenarios see a tighter interaction between the electricity, heating and gas sectors, which calls for a holistic multi-energy system assessment. On these premises, this paper presents an original methodology to quantify the flexibility the gas network can provide to the power system, as well as the constraints it may impose on it, with also consideration of different heating scenarios. This is achieved by a novel multi-stage integrated gas and electrical transmission network model, which uses electrical DC OPF and both steady-state and transient gas analyses. A novel metric that makes use of the concept of zonal linepack is also introduced to assess the integrated gas and electrical flexibility, which is then used to impose gas-related inter-network inter-temporal constraints on the electrical OPF. Case studies are performed for the Great Britain transmission system for different renewables and heating scenarios to demonstrate the proposed integrated flexibility assessment methodology, provide insights into the effects of changes to the heating sector on the multi-energy system’s combined flexibility requirements and capability, and assess how the electrical network can experience local generation and reserve constraints related to the gas network’s lack of flexibility.

Storing renewables in the gas network: modelling of power-to-gas seasonal storage flexibility in low-carbon power systems

Abstract: The power-to-gas (P2G) process, whereby excess renewable electrical energy is used to form hydrogen and/or synthetic natural gas (NG) that are injected, transported, and stored in the gas network, has the prospect to become an important flexibility option for the seasonal storage of low-carbon electricity. This study is the first to model and assess the potential of P2G when combined with gas seasonal storage operation accounting for the two networks' characteristics and constraints (including the amount of hydrogen that can be blended with NG under different gas network conditions). Power system operation with P2G is analysed via a two-stage optimisation based on DC power flow to assess the gas production from otherwise curtailed renewables, also considering impact of P2G on short-term and long-term gas prices. Additionally, impact of P2G on gas network operation and its potentially required re-dispatch are evaluated with a steady-state gas flow model. Case studies conducted on the Great Britain gas and electrical transmission networks quantify benefits and limitations of the integrated usage of P2G with seasonal gas storage under different scenarios. The proposed model thus sets the fundamentals for further development of this emerging technology as a seasonal storage option in low-carbon power systems.

Flexible Distributed Multienergy Generation System Expansion Planning Under Uncertainty

Abstract: A key feature of smart grids is the use of demand side resources to provide flexibility to the energy system and thus increase its efficiency. Multienergy systems where different energy vectors such as gas, electricity, and heat are optimized simultaneously prove to be a valuable source of demand side flexibility. However, planning of such systems may be extremely challenging, particularly in the presence of long-term price uncertainty in the underlying energy vectors. In this light, this paper proposes a unified operation and planning optimization methodology for distributed multienergy generation (DMG) systems with the aim of assessing flexibility embedded in both operation and investment stages subject to long-term uncertainties. The proposed approach reflects real options thinking borrowed from finance, and is cast as a stochastic mixed integer linear program. The methodology is illustrated through a realistic U.K.-based DMG case study for district energy systems, with combined heat and power plant, electric heat pumps, and thermal energy storage. The results show that the proposed approach allows reduction in both expected cost and risk relative to other less flexible planning methods, thus potentially enhancing the business case of flexible DMG systems.

A critical review of Real Options thinking for valuing investment flexibility in Smart Grids and low carbon energy systems

Abstract: This paper aims at serving as a critical analysis of Real Options (RO) methodologies that have so far been applied to the flexible evaluation of smart grid developments and as a practical guide to understanding the benefits but more importantly the limitations of RO methodologies. Hence, future research could focus on developing more practical RO tools for application to the energy industry, thus making the utilization of powerful “real options thinking” for decision making under uncertainty more widespread. This is particularly important for applications in low carbon power and energy systems with increasing renewable and sustainable energy resources, given the different types of uncertainty they are facing in the transition towards a truly Smart Grid. In order to do so, and based on an extensive relevant literature review, the analogies with financial options are first presented, with various assumptions and their validity being clearly discussed in order to understand if, when, and how specific methods can be applied. It is then argued how option theory is in most cases not directly applicable to investment in energy systems but requires the consideration of their physical characteristics. The paper finally gives recommendations for building practical RO approaches to energy system (and potentially all engineering) project investments under uncertainty, regardless of the scale, time frame, or type of uncertainty involved.

Modelling of integrated multi-energy systems: Drivers, requirements, and opportunities

Abstract: There is growing recognition that decarbonisation of existing uses of electricity is only ‘part of the story’ and that closer attention needs to be given to demand for energy in heating or cooling and in transport, and to all the energy vectors and infrastructures that supply the end-use demand. In this respect, concepts such as ‘multi-energy systems’ (MES) have been put forward and are gaining increasing momentum, with the aim of identifying how multiple energy systems that have been traditionally operated, planned and regulated in independent silos can be integrated to improve their collective technical, economic, and environmental performance. This paper addresses the need for modelling of MES which is capable of assessing interactions between different sectors and the energy vectors they are concerned with, so as to bring out the benefits and potential unforeseen or undesired drawbacks arising from energy systems integration. Drivers for MES modelling and the needs of different users of models are discussed, along with some of the practicalities of such modelling, including the choices to be made in respect of spatial and temporal dimensions, what these models might be used to quantify, and how they may be framed mathematically. Examples of existing MES models and tools and their capabilities, as well as of studies in which such models have been used in the authors' own research, are provided to illustrate the general concepts discussed. Finally, challenges, opportunities and recommendations are summarised for the engagement of modellers in developing a new range of analytical capabilities that are needed to deal with the complexity of MES.

Framework for capacity credit assessment of electrical energy storage and demand response

Abstract: The use of electrical energy storage (EES) and demand response (DR) to support system capacity is attracting increasing attention. However, little work has been done to investigate the capability of EES/DR to displace generation while providing prescribed levels of system reliability. In this context, this study extends the generation-oriented concept of capacity credit (CC) to EES/DR, with the aim of assessing their contribution to adequacy of supply. A comprehensive framework and relevant numerical algorithms are proposed for the evaluation of EES/DR CC, with different ‘traditional’ generation-oriented CC metrics being extended and a new CC metric defined to formally quantify the capability of EES/DR to displace conventional generation for different applications (system expansion, reliability increase etc.). In particular, specific technology-agnostic models have been developed to illustrate the implications of energy capacity, power ratings, and efficiency of EES, as well as payback characteristics and customer flexibility (that often also depend on different forms of storage available to customers) of DR. Case studies are performed on the IEEE RTS to demonstrate how the different characteristics of EES/DR can impact on their CC. The framework developed can thus support the important debates on the role of EES/DR for smart grid planning and market development.

Unified Unit Commitment Formulation and Fast Multi-Service LP Model for Flexibility Evaluation in Sustainable Power Systems

Abstract: Classical unit commitment (UC) algorithms may be extremely time-consuming when applied to large systems and for long-term simulations (for instance, a year) and may not consider all the features required for flexibility assessment, including analysis of different reserve types. In this light, this paper presents a novel flexibility-oriented unified formulation of a large-scale scheduling model considering multiple types of plants (including storage) and reserves, which can seamlessly model binary (BUC), mixed integer linear programming (MILP), and relaxed linear programming (LP) UC. Comparisons are carried out on several case studies for a reduced model of Great Britain, assessing loss of accuracy (as measured according to various metrics specifically introduced) against computational benefits in different renewables scenarios with more or less flexible systems. It is demonstrated how the computational time of the LP model is significantly less than the BUC and MILP approaches while capturing with relatively high precision all the relevant flexibility requirements and allocation of multiple types of reserves to different types of plants. The results indicate that the proposed fast LP model could be suitable for various computationally intensive flexibility studies (e.g., Monte Carlo simulations or planning), with significant reduction in simulation time and only minor errors relative to established MILP models.

Demand Response Contracts as Real Options: A Probabilistic Evaluation Framework Under Short-Term and Long-Term Uncertainties

Abstract: This paper aims to set up a probabilistic framework to assess the value of a portfolio of demand response (DR) customers under both operational (short-term) and planning (long-term) uncertainties through real options (ROs) modeling borrowed from financial theory. In an operational setting, DR is considered as an RO contract allowing an aggregator seeking to maximize its revenue to sell flexible demand in the day-ahead market and balance its energy portfolio in the balancing markets. Sequential Monte Carlo simulations (SMCS) are used to value DR activation decisions based on market price evolutions. These decisions combine DR physical characteristics and portfolio scheduling optimization, whereby the aggregator chooses to exercise only the contracts probabilistically leading to a profit, also considering the physical payback effects of load recovery. Sensitivity of profits to changing market conditions and payback characteristics is also assessed. In an investment setting, subject to long-term uncertainties, the value of an investment in DR-enabling technology is quantified through the Datar–Mathews RO approach that applies hybrid SMCS and scenario analysis. The results show how the flexibility value of DR can be highlighted by modeling it as RO, particularly in high volatile markets, and how realistic inclusion of payback characteristics significantly decrease the benefits estimated for DR. In addition, the proposed RO framework generally allows hedging of the risks incurred under long-term and short-term uncertainties.

Distributed Software Infrastructure for General Purpose Services in Smart Grid

Abstract: In this paper, the design of an event-driven middleware for general purpose services in smart grid (SG) is presented. The main purpose is to provide a peer-to-peer distributed software infrastructure to allow the access of new multiple and authorized actors to SGs information in order to provide new services. To achieve this, the proposed middleware has been designed to be: 1) event-based; 2) reliable; 3) secure from malicious information and communication technology attacks; and 4) to enable hardware independent interoperability between heterogeneous technologies. To demonstrate practical deployment, a numerical case study applied to the whole U.K. distribution network is presented, and the capabilities of the proposed infrastructure are discussed.

Flexible distributed multienergy generation system expansion planning under uncertainty

Abstract: A key feature of smart grids is the use of demand side resources to provide flexibility to the energy system and thus increase its efficiency. Multienergy systems where different energy vectors such as gas, electricity, and heat are optimized simultaneously prove to be a valuable source of demand side flexibility. However, planning of such systems may be extremely challenging, particularly in the presence of long-term price uncertainty in the underlying energy vectors. In this light, this paper proposes a unified operation and planning optimization methodology for distributed multienergy generation (DMG) systems with the aim of assessing flexibility embedded in both operation and investment stages subject to long-term uncertainties. The proposed approach reflects real options thinking borrowed from finance, and is cast as a stochastic mixed integer linear program. The methodology is illustrated through a realistic U.K.-based DMG case study for district energy systems, with combined heat and power plant, electric heat pumps, and thermal energy storage. The results show that the proposed approach allows reduction in both expected cost and risk relative to other less flexible planning methods, thus potentially enhancing the business case of flexible DMG systems.

Optimization under uncertainty of thermal storage-based flexible demand response with quantification of residential users' discomfort

Abstract: This paper presents a two-stage stochastic programming model for provision of flexible demand response (DR) based on thermal energy storage in the form of hot water storage and/or storage in building material. Aggregated residential electro-thermal technologies (ETTs), such as electric heat pumps and (micro-) combined heat and power, are modeled in a unified nontechnology specific way. Day-ahead optimization is carried out considering uncertainty in outdoor temperature, electricity and hot water consumption, dwelling occupancy, and imbalance prices. Building flexibility is exploited through specification of a deadband around the set temperature or a price of thermal discomfort applied to deviations from the set temperature. A new expected thermal discomfort (ETD) metric is defined to quantify user discomfort. The efficacy of exploiting the flexibility of various residential ETT following the two approaches is analyzed. The utilization of the ETD metric to facilitate quantification of the expected total (energy and thermal discomfort) cost is also demonstrated. Such quantification may be useful in the determination of DR contracts set up by energy service companies. Case studies for a U.K. residential users' aggregation exemplify the model proposed and quantify possible cost reductions that are achievable under different flexibility scenarios.

Assessing the benefits of coordinated operation of aggregated distributed Multi-energy Generation

Abstract: Evolving planning concepts of future low carbon energy districts take into account multiple energy resources and vectors but, despite the comprehensive approach, they often miss out on capturing interactions between those vectors and do not exploit all the opportunities available by such approach. This paper presents a mixed integer linear programming (MILP) algorithm for modeling operational behavior of aggregated district multi-energy generation units. The resulting aggregated generation portfolio can be seen as a city-level Multi-Energy Virtual Power Plant (MEVPP). Case study results clearly demonstrate benefits of different multi-energy configurations and their interactions under various market conditions, by using an “aggregation benefit matrix” specifically introduced. The outcomes provide strategic indications to energy companies, local authorities, policy makers, etc. in the direction of developing low carbon Smart Cities.

Energy Positive Neighborhoods and Smart Energy Districts : Methods, Tools, and Experienes from the Field

Abstract: Energy Positive Neighborhoods and Smart Energy Districts: Methods, Tools, and Experiences from the Field is a comprehensive guide to this highly interdisciplinary topic. Monti et. al?s combined experience make them the most qualified team of editors to explore the processes and tools involved in creating Energy Positive Neighborhoods and Smart Energy Districts in an urban setting. Tools include: A complete simulation library to quickly support the implementation of a model of the scenarioA set of possible approaches to neighborhood energy optimizationAn open, extensible information model for neighbourhood asset description The structure of this book offers different reading paths to appeal to the very varied audience it addresses. It describes the process of adaption and the challenges faced by the decision makers, and also how simulation, optimisation, ICT approaches and business models are combined in a holistic and pragmatic way. It also offers possible business models and a means to quantify them to complete the development process. This book is suitable for students on muti-disciplinary energy engineering courses, energy practitioners, ICT vendors aiming to develop new services to target the building industry, and decision makers aiming to structure an urban renovation program.Delivers a significant amount of exclusive knowledge on the topics of energy positive neighborhoods and smart energy districtsAllows readers to grasp the complexity of this interdisciplinary topic by providing access to well-structured processes and tools Includes real life examples of the transformation of two demonstration sites that illustrate the concepts discussed to add context and value to their implementation

A severity risk index for high impact low probability events in transmission systems due to extreme weather

Abstract: It is evident worldwide that high-impact, low-probability (HILP) events, such as associated to extreme weather, can have disastrous consequences on power systems resilience. In this paper, we propose a Severity Risk Index (SRI) that with the support of smart grid technologies (e.g., real-time monitoring) is capable of providing an indication of the evolving risk of power systems subject to HILP events in a smart and adaptive way, thus potentially contributing to effective decision-making to mitigate such risk. Specific applications considered here refer to windstorm events, for which purpose the proposed SRI is embedded in a Sequential Monte Carlo simulation for capturing the spatiotemporal effects of windstorms passing across transmission networks. Latin Hypercube Sampling and backward scenario reduction method are used to produce a computationally tractable number of representative scenarios for SRI computation. The IEEE 24-bus reliability test system is used to demonstrate the effectiveness of the proposed SRI.

Assessing the impacts of extreme temperatures and water availability on the resilience of the GB power system

Abstract: Extreme weather events or more in general changing environmental conditions (for instance due to climate change) might have significant impacts on future power systems, threatening their resilient operation. In this context, this paper provides a quantitative analysis of the temperature and water availability effects on power system resilience. Differently from most existing work that only addresses the impact on individual power plants and independently of the context, a system level assessment is conducted here through a time-series model that specifically considers the temperature sensitivity and the impact of water availability on the cooling systems of all conventional thermal power plants, as well as the temperature sensitivity of line capacities and of electrical demand throughout the network. Sequential Monte Carlo Simulation (SMCS) is used to capture the stochastic impacts of such phenomena and derive relevant impact metrics. The model is demonstrated on a 29-bus reduced representation of the Great Britain (GB) transmission network. Several future scenarios for future generation and demand are formulated with different corresponding weather parameter. The results help recognize the vulnerability and resilience of future GB power systems to extreme weather events under different conditions.

Modelling and assessment of business cases for smart multi-energy districts

Abstract: This work presents a stochastic mixed integer linear program based model, for optimization and business case assessment of smart multi-energy districts (including electricity, heat and gas). The model is general and extensible, and can include multiple types of multi-energy generation and consumption. In particular, it is capable of co-optimization of energy and capacity, for participation in multiple energy/reserve/capacity markets. Further, the model incorporates a level-of-aggregation approach, which facilitates modelling and assessment of physical and virtual aggregation within districts. The model is demonstrated through application to a case study district in the Irish energy system. Prices from the various relevant energy markets and charging regimes are presented, before the results of optimization with respect to various business cases are explored. The value of optimization on retail prices, various energy/capacity-related markets/charging regimes, and of aggregation is demonstrated. Directions for further application of the model are detailed.

Towards sustainable urban energy systems: High resolution modelling of electricity and heat demand profiles

Abstract: Urban energy systems are attracting more and more attention owing to the challenges as well as potential to make them more sustainable. In particular, decarbonisation may be achieved by deploying a portfolio of multi-energy technologies (electricity, heat, cooling, gas, transport), for both distributed (e.g., PV, heat pumps, electric vehicles, thermal and electrical storage, etc.) and centralised (e.g., community-level or city-level energy systems supplied by cogeneration, trigeneration, etc.) applications. This calls for high resolution modelling from both temporal and spatial perspectives, suitable to capture infrastructure impact and requirements as well as intertemporal characteristics of new technologies (especially for storage). This paper presents an initial investigation into a modelling approach which provides high spatial and temporal electricity and heat demand profiles taking proper account of various consumption characteristics of different customer sectors, with application to the Greater Manchester's metropolitan area. A general framework for evaluating the different performances and requirements of city-level multi-energy system is also presented.

Exploring flexibility of aggregated residential electric heat pumps

Abstract: The integration of Renewable Energy Sources (RES) and the electrification of the heating and transportation sectors are stressing the operation of current power systems and call for more flexibility. Domestic electric heat pumps (EHP), which are expected to be widely deployed in the future, can be considered as one potential source of such system flexibility. However, this can also lead to negative impacts for building occupant comfort and to increased peak demand, through reduction in load diversity. Such impacts may be mitigated through the deployment of Thermal Energy Storage (TES), although the benefit this brings is not well understood. Therefore, this paper presents a method to quantify the impact on occupant comfort level and load diversity, through various payback metrics. A validated model is then used to simulate the extraction of reserve capacity from a cluster of 500 domestic buildings with EHPs and different configurations of space heating buffer. Performance in terms of occupant comfort and payback is evaluated.

Assessment of the impact of heating on integrated gas and electrical network flexibility

Abstract: Power systems with increasing flexibility requirements are increasingly relying on gas turbines to follow the net-load and the gas network to support unforecast changes in gas turbine generation. In addition, the role of the gas network in supplying fuel for the heating sector means that alternative heating scenarios can impact the gas network's ability to provide this flexibility. In this light, this paper presents a novel methodology for the assessment of the impact of heating on the integrated flexibility of the gas and electrical transmission networks. The model considers the transport requirements of gas for the heating sector as well as the regional distribution of gas within the gas network in the context of zonal linepack distribution. Electrical network modelling is conducted using a DC OPF while the gas network flexibility is assessed using a novel iterative steady-state gas flow methodology. Case studies are performed on the British networks and highlight how in energy systems with a large heat-based gas demand then extreme cold days can reduce the flexibility of the gas network while, on milder days, the use of gas turbines in supplying an electrified heating sector can lead an increase in linepack swing.

Integrated electricity and heat active network management

Abstract: Integration of electricity and heat is one of the most promising options to achieve a sustainable low-carbon energy system in presence of Renewable Energy Sources (RES). In this respect, there are ongoing studies worldwide with the aim of assessing the performance of fully-integrated energy systems. However, while they are encouraging in terms of results, the planning is generally made without considering electrical network constraints, missing to capture the real technical and economic performance of integrated systems. In this paper, a new model for integrated electricity and heat active network management based on a dual-horizon Dynamic AC OPF is presented. The model is capable to plan and optimise operations for different multi-energy technologies considering both electrical network and associated inter-temporal constraints and uncertainties, allowing assessing the potential benefits and available flexibility brought by energy integration.

Integrated electrical and gas network flexibility assessment in low-carbon multi-energy systems

Abstract: In power systems with increasing variable renewable sources, gas generation is playing an increasingly prominent role in providing flexibility to meet net-load requirements. The flexibility provided by the gas turbines in turn relies on the flexibility of the gas network. While there are several discussions on the gas network's ability in providing this operational flexibility, this has not been clearly modelled or quantified. In addition, the gas network may also be responsible for supplying heating technologies, and low-carbon scenarios see tighter interactions between the electricity, heating and gas sectors, calling for a holistic multi-energy system assessment. On these premises, this paper presents a methodology to quantify the flexibility the gas network can provide to the power system, as well as the constraints it may impose on it, with also consideration of different heating scenarios. This is achieved by a multi-stage integrated gas and electrical transmission network model, using electrical DC OPFs and both steady-state and transient gas analyses. A novel metric making use of the concept of zonal linepack is introduced to assess the integrated gas and electrical flexibility, which is then used to impose gas-related internetwork inter-temporal constraints on the electrical OPF. Case studies performed for the Great Britain transmission system demonstrate the proposed integrated flexibility assessment, provide insights into the effects of changes to the heating sector on the multi-energy system's combined flexibility requirements and capability, and assess how the electrical network can experience local generation and reserve constraints related to the gas network's lack of flexibility.

Distribution network support from multi-energy demand side response in smart districts

Abstract: This work investigates from a techno-economic perspective the potential for groups of smart buildings distributed throughout an area to provide Demand Side Response (DSR) as a means to increase electricity distribution network capacity. More specifically, intelligent multi-energy flows between buildings within a smart district are optimized with the aim of reducing energy costs for end-users and network costs for Distribution Network Operators (DNOs). For this purpose, an optimization methodology that captures the impacts of DSR by explicitly modelling (ii) interactions between different energy vectors through an integrated electricity-heat-gas model, (ii) security limits and (iii) DNO investment decisions and preferred deployment of DSR, is proposed. The methodology is illustrated with a real UK multi-energy district where an intelligent information platform (i.e., the District Information Modelling and Management for Energy Reduction (DIMMER) platform under development in the homonymous European project) is being tested. The significant flexibility of the smart district to provide DSR services without compromising end-user comfort levels (i.e., avoiding load curtailment) by intelligently exchanging different energy vectors is demonstrated. The results highlight a strong business case for DSR, as the associated deployment costs are minimal thanks to the flexibility of the smart district, while network savings are significant.