A transactive energy modelling and assessment framework for demand response business cases in smart distributed multi-energy systems
Introduction
Legislative efforts to increase the sustainability of energy systems (by reducing emissions), combined with constant consumer pressure to minimise costs and expectations of high levels of reliability are leading to several broad trends. Firstly the penetration of variable, non-dispatchable renewable energy sources (RES) (utility-scale and distributed) and electric heating and transport are increasing. These trends produce two countervailing further trends, i.e., increased demand for flexibility, but also an increase in technologies capable of supplying flexibility. Demand for flexibility is expected to rise as network/system issues increase. More RES will increase variability and uncertainty in system operation, thereby increasing the requirement for balancing services; at the same time, the business cases for traditional providers of such services (flexible generation) will be eroded [1,2]. Further, increased and bi-directional electricity flows at the distribution level can produce thermal and voltage issues at the district level [3,4]. At the same time, greater electrification of heating and transport as well as increased penetration of smart grid technologies in local RES (e.g., photovoltaic inverters) will contribute to a trend for distributed multi-energy systems (DMES) to potentially provide flexibility via demand response (DR). DR from DMES can be understood as being derived from several factors, namely, storage, substitution, curtailment and power factor correction [5,6]. Utilising a multi-energy view [7], storage may be of electricity, or some derived energy vector/product, such as heat, and substitution may be of one fuel for another (e.g., electricity for fuel oil), to produce the same final product (e.g., mobility). Curtailment may relate to some energy service, such as thermal comfort [8,9], whilst power factor correction (of a building, or a district) may be enacted by some power electronic interface (e.g., from a battery or solar photovoltaic connection) [6]. Clearly increased electrification of transport and heating, together with developments in information and communication technology (ICT) and exploitation of multi-energy resources will result in more opportunities to provide DR from DMES [6,[10], [11], [12], [13]].
Although flexibility from these DMES (e.g., districts or community based energy systems) may be considered more attractive than other sources of flexibility (given their location on distribution networks, which introduces the possibility of providing local services) there are multiple and various barriers facing such new players in the developing “smart grid” [14,15]. Focusing on districts (which are a special case of DMES and which, when augmented with appropriate technologies, may be considered a component of larger “smart” constructs, such as cities [7]) a key barrier is the lack of appropriate techno-economic models. Modelling and assessment of DR from such “smart energy districts” requires a holistic framework, covering both physical and commercial aspects. On the physical side, models must capture the multiple sources of (multi-energy) flexibility which can be exploited [8,16]. Given the importance of physical and virtual (commercial) aggregation with respect to heat, as well as gas and electricity, the framework should allow for modelling of various permutations of aggregation arrangements. Given the complexity of optimisation problem formulation, especially if a stochastic optimisation is being employed with many sources of storage, such a method should be flexible and scalable. On the commercial side there is then a requirement for methods which can breakdown the complexity of DR business cases (covering energy, capacity, or flexibility services [17]) and highly networked energy systems. Such complexity may be even more significant if costs related to DR exercise need to be shared [[18], [19], [20]]. Further, connections can be expected to increase between markets for substitutable energy vectors and fuels in a multi-energy context [7,[21], [22], [23]]. Understanding and modelling this complexity has been identified as a priority by both regulatory bodies and academics [[24], [25], [26]].
Another necessary feature for a smart energy district modelling and assessment framework is the ability to model methods for sharing the benefits of flexibility exploitation between district consumers and their commercial partner (e.g., aggregator or retailer). In the existing literature a qualitative examination of how benefits from ‘co-provision’ (by consumers, as well as energy retailers/energy service companies) of energy or energy services may be shared between relevant actors has been undertaken in Refs. [27,28]. For smart districts the provision of flexibility services to upstream parties must also be considered. In this regard, theoretical and practical attention has been given to methods for controlling/coordinating prosumers, ranging from direct load control, to coordination via price signals (from national and local energy markets) [29,30]. Some of these approaches (i.e., game theory based approaches) consider how benefits should be shared amongst consumers and (in some cases) an aggregator party [29]. These approaches draw on the rich field of game theory [31] and can ensure a ‘fair’ distribution of benefits. However, this comes at the price of great complexity and requires significant communication and computation capabilities within the district. Detailing of further quantitative methods for sharing benefits from exploitation of flexibility will therefore be beneficial. This is particularly true if consumers do not wish to adopt the technology required for them to act as the autonomous intelligent agents required for game theoretic approaches. Our work thus proposes a novel approach that bridges the existing gaps while at the same time being practical and of straightforward applicability without adding technology and communication complexity.
Thus there is clearly a need for more quantitative methods for sharing benefits of flexibility exploitation between DR providers (consumers) and their commercial agents. Such methods warrant consideration given the fundamentally different nature of flexibility exploitation business cases to energy service provision business cases, as the former may, for example, often increase energy consumption, as flexibility is employed to import and store energy at lower price periods, incurring storage losses (see Section 4.3).
In this paper, addressing the deficits identified above modelling and assessment framework for smart DMES, with specific application to the particularly important case of smart districts, is presented. The framework follows a transactive energy based approach. Following the definition of the GridWise Architectural Council, we define transactive energy as “a set of economic and control mechanisms that allow the dynamic balance of supply and demand across the entire electrical infrastructure using value as a key operational parameter” [32]. The approach is ideally suited given that smart DMES are subject to multiple markets/mechanisms. Particular contributions are:
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A proposed aggregation modelling methodology which, together with previously presented stochastic, physical models can capture and exploit distributed energy flexibility to provide multiple services. This method builds on the electricity network and commercial aspects of aggregation presented in Ref. [33] to include multi-energy conversion, storage and demand.
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A novel, modular and extensible value mapping methodology which can mitigate complexity by clearly defining interactions between actors along the transactive energy value chain.
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New methods for sharing the benefits of distributed energy flexibility exploitation between DR providers and their commercial agents.
In the remainder of this paper, Section 2 introduces the modelling and assessment framework for DMES and in particular smart energy districts, including the district optimisation model, value mapping model and profit sharing model. Section 3 introduces a case study, of a district in the French system, on which the framework can be demonstrated, for various business cases. Section 4 presents the results of the case study. Section 5 concludes and highlights future avenues of research.
Section snippets
Transactive energy modelling and assessment framework
The transactive energy modelling and assessment framework proposed here is made up of several models. The framework, together with the main inputs to each component model, is shown in Fig. 1, and is composed of the following components:
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DMES aggregation and multi-service optimisation model, exemplified for the sake of clarity on a district: The model utilises the physical energy/reserve optimisation model detailed in Refs. [8,16] 1
Case study description
In this section, the use of the proposed modelling and assessment framework is exemplified below in the analysis of six business cases undertaken for a French district of commercial buildings.
Results and discussion
Below, in Section 4.1, the RAD cash flows from the various business cases are presented and discussed. Subsequently, in Section 4.2, the effect of district optimisation on the various other actors of the energy system is studied in an example for the ‘All’ business case (optimisation on all price signals). Then in Section 4.3, the retail tariffs and consumer cash flow resulting from the profit sharing model are presented. Finally in Section 4.4 the NPV of the various business cases are
Conclusions
This paper presents a novel and powerful modelling and assessment framework for assessment of business cases for DMES in a transactive energy context, with especially important application to smart energy districts. The framework incorporates an aggregation mapping methodology that can be incorporated into relevant multi-service price-based optimisation models, with tools for dealing with the complexity of the energy system and of profit sharing between commercial partners, as well as for
Acknowledgments
The authors wish to acknowledge the funding from the EU Commission under the FP7 COOPERATE (Grant n. 600063) and DIMMER (Grant n. 609084) projects, and the H2020 SEAF project (Grant n. 696023). The authors also acknowledge funding from the EPSRC under the MY-STORE project (EP/N001974/1), and thank colleagues in all projects for the helpful discussions.
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