Multi-vector Energy System Research Articles

The role of hydropower in decarbonisation scenarios

An increased penetration of renewable energy sources is essential for the energy transition. A major role will be played by wind and solar, as they are widely available. Hydropower is another crucial resource, currently covering large shares of power generation (e.g., Norway, Italy, Brazil). Despite little expected growth, in a context of increasing electrification, improved integration of hydropower can play a critical role thanks to programmable operation. This work addresses the modelling of hydropower flexibility in energy system models and analyses the impact of hydropower operation on CO2 emission-constrained scenarios. To implement the study, a detailed dataset of the Italian programmable hydroelectric plants is created, using open-source information, covering location, rated power, and storage capacity. Inflow timeseries are derived from historical operational data. These new sets of data are employed in OMNI-ES (a multi-node, multi-sector, and multi-vector energy system model) to study optimal configurations and operation of the Italian energy system in decarbonisation scenarios, such as net-zero-CO2 and Fit-for-55 targets. Considering different operational strategies and multiple historical reference years (impacting the inflow), results demonstrate significant changes in hydropower behaviour and highlight its relevance as zero-carbon resource in terms of both power and energy output, influencing the installation of other technologies.

Flexible copper: Exploring capacity-based energy demand flexibility in the industry

Different forms of flexibility can help in balancing variable generation. This work focuses on industrial demand-side flexibility applied in copper production, which is expected to grow for the build-out of green technologies.This study assesses the potential of capacity-based energy demand flexibility (over-sizing production processes) in an industry embedded in fully renewable energy systems. For this, an optimization model for multi-vector energy systems planning is extended so that it also includes the sizing and operation of a production process. A case study is presented for copper production, with greenfield results until 2050.Results show that flexibility at the concentration and refining stages belongs to the cost-optimal system design, at least over the next decade. At current costs, the potential cost savings in the energy system for the production process through capacity-based demand flexibility range from 5 % to 12 %, depending on the technology scenario. These potential savings are expected to decrease over time if cost reductions of renewable energy supply and storage technologies materialize. Technology scenarios considering seawater pumped-hydro energy storage yield lower costs over the entire projected period.

Day-ahead economical planning of multi-vector energy district considering demand response program

Increasing technology developments and economic considerations have also made the use of renewable energy sources (RESs) and energy storage systems (ESSs) inevitable. To achieve the holistic planning of a microgrid consisting of several energy resources, this paper proposes a novel multi-vector energy system based on electricity, heating, and water generation sources. To supply electrical energy from heat generation units, a water-power nexus (WPN), an ESS, photovoltaic (PV) sources, and a combined heat and power (CHP) system are used. Considering the importance of Hydro generation in planning, combined energy-water and water-only systems are used to supply water demand, while heat-power and heating generation systems are used to supply heating. To make the model more realistic, the effects of the valve point, maximum and minimum generation constraints, increasing/decreasing rate, energy, water, and heating demand balance were taken into account. The main objective function of this study was to minimize multi-vector energy system costs in 24 h. To promote demand-side performance, the price-based demand response program was used to reduce the final costs in the entire study period. The proposed models have been formulated as a mixed-integer linear problem solved by the CPLEX solver in GAMS. Simulation results show that the use of RES and their proper management can reduce the generation of a thermal generator, which reduces the costs considerably. With the demand response program, the costs were reduced by 1.03%. This paper presents a systematic method for optimal system control that can provide a regulatory basis for the use of integrated generation sources.

Exploring Potential Gains of Mobile Sector-Coupling Energy Systems in Heavily Constrained Networks

The coincidence of high levels of variable, non-dispatchable generation from renewable energy sources (RESs) and congested electricity networks imposes significant constraint payments (CP) on electricity system operators (ESOs) which ultimately is charged to the customers. This paper is inspired by this challenge and proposes an integrated electricity, gas, and transportation energy system taking advantage of power-to-gas (P2G) facilities and electricity/gas storage devices to enhance operational efficiency. It proposes mobile gas storage systems (MGSs) that can store and carry liquid hydrogen or liquefied natural gas (LNG) to the load points or remote locations without access to the gas network. So, the green energy of RESs in the form of gases can be injected, transported, and reutilized in the natural gas network or stored in MGS facilities. Besides, the mobile electricity storage system (MES) can directly store the redundant electricity produced by RESs, and the railway transportation system carries both the MESs and MGSs to the load point of electrical and gas systems. The proposed model reflects CP to wind in the marketing phase and considers incentives for the hydrogen-burning generators. Also, a stochastic platform is employed to capture the inherent uncertainties in the predicted values of the load and RESs’ generation. The model is formulated as a mixed-integer second-order cone programming problem and tested on an IEEE 118-bus system integrated with a 14-node gas network and a railway system. The result shows that employing the multi-vector energy system (MVES) elements reduces the total operational cost by 47%, and the CP to wind is reduced by 99.8% by absorbing almost the whole green energy of wind farms while relieving congestion in the electrical grid.

Planning of Multi-Vector Energy Systems with High Penetration of Renewable Energy Source: A Comprehensive Review

The increasing use of high shares of renewable energy sources (RESs) in the current electricity network introduces challenges to the design and management of the electricity network due to the variation and uncertainty nature of the RESs. Some existing energy infrastructures, such as heat, gas, and transport, all have some level of inbuilt storage capacity and demand response (DR) potentials that can be exploited in an energy system integration to give the electricity network some level of flexibility and promote an efficient transition to a low-carbon, resilient, and robust energy system. The process of integrating different energy infrastructure is known as multi-vector energy systems (MESs). This paper reviews different studies on the planning of MESs using the energy hubs (EHs) approach. The EHs model used in this paper links different energy vectors such as gas, electricity, and heat energy vectors in its planning model, as opposed to planning each energy vector independently, in order to provide more flexibility in the system, minimise total planning cost, and encourage high penetration of renewable energy source for future energy demands. In addition, different uncertainty modelling and optimization methods that have been used in past studies in planning of EH are classified and reviewed to ascertain the appropriate techniques for addressing RESs uncertainty when planning future EH. Numerical results show 12% reduction in the planning cost in the case of integrated planning with other energy vectors compared to independent planning.

The Landscape and Roadmap of the Research and Innovation Infrastructures in Energy: A Review of the Case Study of the UK

Research and development are critical for driving economic growth. To realise the UK government’s Industrial Strategy, we develop an energy research and innovation infrastructure roadmap and landscape for the energy sector looking to the long term (2030). This study is based on a picture of existing UK infrastructure on energy. It shows the links between the energy sector and other sectors, the distribution of energy research and innovation infrastructures, the age of these infrastructures, where most of the energy research and innovation infrastructures are hosted, and the distribution of energy research and innovation infrastructures according to their legal structure. Next, this study identifies the roadmap of energy research and innovation infrastructures by 2030, based on a categorisation of the energy sector into seven subsectors. Challenges and future requirements are explored for each of the sub-sectors, encompassing fossil fuels and nuclear energy to renewable energy sources and hydrogen, and from pure science to applied engineering. The study discusses the potential facilities to address these challenges within each sub-sector. It explores the e-infrastructure and data needs for the energy sector and provides a discussion on other sectors of the economy that energy research and innovation infrastructures contribute to. Some of the key messages identified in this study are the need for further large-scale initiative and large demonstrators of multi-vector energy systems, the need for multi-disciplinary research and innovation, and the need for greater data sharing and cyber-physical demonstrators. Finally, this work will serve as an important study to provide guidance for future investment strategy for the energy sector.

A multi-model method to assess the value of power-to-gas using excess renewable

Power-to-Gas (P2G) is a process that produces a gas from electricity, which is most commonly hydrogen via electrolysis. While some studies have considered hydrogen as a power-to-power storage vector, it could also be used as a fuel across the energy system, for example for transport or heat generation. Here, two energy models are used to explore the potential contribution of P2G as a cost-effective source of hydrogen, particularly for future energy systems with high variable renewable energy (VRE) in which there are occasional periods when electricity supply exceeds demand. A detailed electricity system model is iterated with a multi-vector energy system model using a soft-linking approach. This iterative approach addresses shortcomings in each model to better understand the optimal capacity of P2G and the potential economic capture rate of excess VRE. The modelling method is applied to Great Britain in 2050 as a case study. A substantial proportion of excess VRE in 2050 can be captured by P2G, and it is economically competitive compared with alternative sources. Moreover, the effectiveness and economic viability of P2G for reducing excess renewable is robust at even very high levels of renewable penetration. • We soft-linked long-term energy system model (UK TIMES) with power system model to examine value of Power-to-Hydrogen(P2G). • The variability of VRE and the potential curtailment are investigated at spatially and temporally explicit details. • P2G using excess renewable could be an important hydrogen supply and also help integrate high level of VRE. • P2G using excess renewable is found as the most cost-effective technology for generating hydrogen for 2050 in GB case study. • P2G would accounts for 87%-100% of total excess renewable in different scenarios, with the lowest production cost.

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Microgrid (MG) is one of the most effective solution to integrate distributed renewable energy into power system. However, modern MG has several technical challenges such as migration to multi vector energy system, increasing renewable energy penetration, and the uncertainties that arise from the large-scale incorporation of renewable energy. Recent development of multi-microgrid (MMG) could potentially mitigate these challenges. MMG has obvious advantages in improving the system stability, reliability, economy, and energy efficiency of power grid operation through autonomous management and coordinated control among networked MGs. In the meantime, uncertainties from many elements and operators in the MMG system also pose huge challenges in modeling. This paper will present and review typical architecture of MMG, including physical layer, information layer and application layer. Moreover, this paper will critically review and analyze challenges in uncertainty modelling and solution in MMGs. Finally, the paper will also discuss future research areas and development trends of MMG.

Arranging university semester date to minimize annual CO2 emission: A UK university case study

SummaryExisting methods of reducing carbon emissions on campus often require substantial investment, and the potential opportunities for carbon dioxide and energy savings in universities with existing infrastructure have not been considered in much detail. This work fills this gap by considering an indirect and soft demand response strategy, i.e., semester arrangement. To identify the optimal operational strategy of a realistic campus-level multi vector energy system (MES) in Scotland based on CO2 emissions, an original tool is presented. Two conclusions can be drawn safely from the case study. Firstly, changing the operational mode of the university could significantly reduce CO2 emissions. Secondly, considering the difference between average emission factor (AEF) and marginal emission factor (MEF) in the power grid, the different operational modes will bring different electricity/heat demands and also affect carbon emissions. The work opens up a new perspective for worldwide university operators who are considering reducing CO2 emissions.

Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT’s predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction. This paper is motivated by the fact that practical IESs suffer from multiple uncertainty sources, and complicated surrounding environment. To address this problem, a novel DT-based day-ahead scheduling method is proposed. The physical IES is modelled as a multi-vector energy system in its virtual space that interacts with the physical IES to manipulate its operations. A deep neural network is trained to make statistical cost-saving scheduling by learning from both historical forecasting errors and day-ahead forecasts. Case studies of IESs show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%, comparing to the existing forecast-based scheduling methods. It is also found that both electric vehicles and thermal energy storages play proactive roles in the proposed method, highlighting their importance in future energy system integration and decarbonisation.

Integration of seawater pumped storage and desalination in multi-energy systems planning: The case of copper as a key material for the energy transition

Copper is required for the transformation of global energy systems, and its production is intensive in water and energy. Several studies have investigated the design of renewable energy systems for copper production, aiming at reducing its environmental footprint. Here, we present the first integrated design for desalinated water and energy supply that considers all forms of energy required in the copper production process. For this, we develop an optimization model for planning integrated multi-vector energy and water systems. The model includes -for the first time in an energy system planning model- a concept for integrated pumped-hydro storage using sweater and reverse osmosis desalination. Our results show that water-energy systems for copper production based exclusively on renewables can today achieve costs as low as those of conventional fossil-based systems, when integrating multi-vector planning and seawater pumped-hydro storage. For a case study in Chile and in fully renewable scenarios, the specific cost of supplying energy and desalinated water decreases from 520–670 € per ton of copper at current costs to 330–360 by 2050. By 2030, using seawater pumped-hydro storage makes a fully renewable, multi-energy scenario the least-cost alternative. Such an integrated system is an enabler for reducing the environmental footprint that copper brings into the global energy transition.

The future is flexible? Exploring expert visions of energy system decarbonisation

Decarbonising the energy system means moving from a centralised, fossil fuel-dependent one to one based primarily on renewable energy. Visions of whole system change focus on socio-technical concepts like electrification and flexibility, rather than single technologies such as nuclear or solar. The role that technological niches might play in relation to such systemic socio-technical visions is under-researched. Traditionally, technological niches are thought to nurture new ‘proto-regimes’, supported by coherent visions of how technologies will help solve problems within existing socio-technical regimes. However, the relationship between socio-technical visions like flexibility and individual demonstrator projects is more complex. Interviews with experts from the FLEXIS energy systems project (south Wales, UK) show how future visions associated with a range of demonstrator projects are being developed against the backdrop of a system-level vision of a flexible, multi-vector energy system. Interview data also shows how the development of visions for demonstrators draws on social learning about place and societal values overlooked in systemic visions. Such social learning can incorporate critical reflection on key assumptions that underlie dominant system-level visions. The extent such learning is possible through the development of potential niches, however, is subject to limitations that invite further research.

Investment planning in multi-vector energy systems: definition of key performance indicators

Investment planning in multi-vector energy systems: definition of key performance indicators

GA Optimization Method for a Multi-Vector Energy System Incorporating Wind, Hydrogen, and Fuel Cells for Rural Village Applications

Utilization of renewable energy (e.g., wind, solar, bio-energy) is high on international and governmental agendas. In order to address energy poverty and increase energy efficiency for rural villages, a hybrid distribution generation (DG) system including wind, hydrogen and fuel cells is proposed to supplement to the main grid. Wind energy is first converted into electrical energy while part of the generated electricity is used for water electrolysis to generate hydrogen for energy storage. Hydrogen is used by fuel cells to convert back to electricity when electrical energy demand peaks. An analytical model has been developed to coordinate the operation of the system involving energy conversion between mechanical, electrical and chemical forms. The proposed system is primarily designed to meet the electrical demand of a rural village in the UK where the energy storage system can balance out the discrepancy between intermittent renewable energy supplies and fluctuating energy demands so as to improve the system efficiency. Genetic Algorithm (GA) is used as an optimization strategy to determine the operational scheme for the multi-vector energy system. In the work, four case studies are carried out based on real-world measurement data. The novelty of this study lies in the GA-based optimization and operational methods for maximized wind energy utilization. This provides an alternative to battery energy storage and can be widely applied to wind-rich rural areas.

Integrated Analysis and Planning of Energy Conversion and Storage Devices in Multi-vector Energy Systems

Multi-vector energy systems take into account synergies between different energy vectors (cooling/heat/electricity/gas), which will increase the flexibility for equalising the fluctuations from the renewable energy, and thus facilitate the penetration of renewable energy. This paper focuses on developing whole-system analysis and planning methods of energy conversion and storage devices in multi-vector energy systems, to achieve an overall optimum of energy systems.Various energy conversion and storage devices (such as photovoltaic, CHP, gas boilers, battery energy storage systems (BESS), ice storage and so forth) are planned coordinately as an integrated whole. According to the electricity demand during the planning period and the power and energy balance between supply and demand, the size of energy conversion and storage devices are determined to improve the energy efficiency and the energy supply reliability, as well as mitigate carbon emissions.

The importance of gas infrastructure in power systems with high wind power penetrations

Gas fired generation currently plays an integral support role ensuring security of supply in power systems with high wind power penetrations due to its technical and economic attributes. However, the increase in variable wind power has affected the gas generation output profile and is pushing the boundaries of the design and operating envelope of gas infrastructure. This paper investigates the mutual dependence and interaction between electricity generation and gas systems through the first comprehensive joined-up, multi-vector energy system analysis for Ireland. Key findings reveal the high vulnerability of the Irish power system to outages on the Irish gas system. It has been shown that the economic operation of the power system can be severely impacted by gas infrastructure outages, resulting in an average system marginal price of up to €167/MWh from €67/MWh in the base case. It has also been shown that gas infrastructure outages pose problems for the location of power system reserve provision, with a 150% increase in provision across a power system transmission bottleneck. Wind forecast error was shown to be a significant cause for concern, resulting in large swings in gas demand requiring key gas infrastructure to operate at close to 100% capacity. These findings are thought to increase in prominence as the installation of wind capacity increases towards 2020, placing further stress on both power and gas systems to maintain security of supply.

Modelling, assessment and Sankey diagrams of integrated electricity-heat-gas networks in multi-vector district energy systems

The widespread use of decentralised multi-energy supply solutions such as gas-fired Combined Heat and Power (CHP), heat pumps, gas boilers, and so forth is more and more increasing the linkages between electricity, heat and gas distribution networks. However, there is currently no model able to model the three networks in an integrated manner and with a suitable level of detail for operational purposes. A multi-temporal simulation model, which has been implemented in a relevant MATLAB-Excel VBA tool, is presented in this paper to carry out integrated analysis of electricity, heat and gas distribution networks, with specific applications to multi-vector district energy systems. The network linkages have been modelled through a multi-vector efficiency matrix specifically developed to map the transformation of final demands into network energy flows while taking into account the inter-network locations of the individual supply technologies. The relevant coupled electrical, heat and gas flow equations have been solved simultaneously using a Newton–Raphson approach. A real case study of a district multi-energy system in the Campus of the University of Manchester illustrates the quantitative use of the model in different scenarios for technical, economic and environmental studies. Sankey diagrams of the energy flows across the networks are also presented to give a visual picture of the multi-energy interactions and losses in the district in different scenarios. The model can be flexibly adapted to generic network topologies and multi-energy supply technologies, and can thus be used for practical operational implementations as well as to inform planning of low carbon multi-vector energy systems.