36 research outputs found
The Potential for Energy Arbitrage Using Battery Energy Storage Systems in Norwegian Power Markets : An Economic Viability Study through Financial Valuation
arbitrage, the process of storing energy when prices are low and offering it when prices
are high, has, through increased electricity prices and price volatility, shown greater economic
potential over the past couple of years. In light of these developments, this study analyzes the
economic viability, through a financial valuation, of a 10MW/10MWh Battery Energy Storage
System (BESS) performing energy arbitrage in the Norwegian power markets over a 30-year
project. To account for the latest developments in electricity prices and evaluate the economic
viability of the BESS, the study incorporates 2022 electricity price data. Furthermore, the analysis
includes electricity price data from the period of 2016-2019 to assess the BESS's economic
viability in the event of a return to historically “normal” Norwegian electricity prices. The study
aims to present a comprehensive and holistic valuation of the BESS through the inclusion of all
factors affecting the profits generated and the related costs of performing the energy arbitrage. The
optimal energy arbitrage trading pattern is identified through a Mixed-Integer Nonlinear
Programming (MINLP) model, and the resulting trading profits are valued through a Discounted
Cash Flow (DCF) encompassing all relevant expenditures. The discount rate in the DCF is derived
from an estimated Weighted Average Cost of Capital based on a Comparable Companies Analysis.
The results from the analysis show that a BESS performing energy arbitrage in the Norwegian
power markets is not economically viable with the current BESS cost estimations and power
market conditions. The results for the 2022 electricity price scenario show the greatest promise in
the southern price zones of Norway due to the historically high electricity prices and price
volatility. However, the Net Present Value (NPV) of the cash flows for the BESS in the best
performing price zone is still significantly negative. With optimal trading profits of 39.6 MNOK,
the best performing project generates a NPV of -120.4 MNOK when considering all Capital
Expenditure (CAPEX), Operations and Maintenance (O&M) costs, and trading profits. When
utilizing 2016-2019 electricity price data, the results worsen significantly due to the lower
electricity price and price volatility in the period, resulting in a total trading profit of 2.3 MNOK
and a total NPV of -157.7 MNOK for the BESS in the best performing price zone.nhhma
Optimization of Electric-Vehicle Charging: scheduling and planning problems
The progressive shift from traditional vehicles to Electric Vehicles (EVs ) is considered one of the key measures to achieve the objective of a significant reduction in the emission of pollutants, especially in urban areas. EVs will be widely used in a not-so-futuristic vision, and new technologies will be present for charging stations, batteries, and vehicles. The number of EVs and Charging Stations (CSs) is increased in the last years, but, unfortunately, wide usage of EVs may cause technical problems to the electrical grid (i.e., instability due to intermittent distributed loads), inefficiencies in the charging process (i.e., lower power capacity and longer recharging times), long queues and bad use of CSs. Moreover, it is necessary to plan the CSs installation over the territory, the schedule of vehicles, and the optimal use of CSs.
This thesis focuses on applying optimization methods and approaches to energy systems in which EVs are present, with specific reference to planning and scheduling decision problems.
In particular, in smart grids, energy production, and storage systems are usually scheduled by an Energy Management System (EMS) to minimize costs, power losses, and CO2 emissions while satisfying energy demands. When CSs are connected to a smart grid, EVs served by CSs represent an additional load to the power system to be satisfied, and an additional storage system in the case of vehicle-to-grid (V2G) technology is enabled. However, the load generated by EVs is deferrable. It can be thought of as a process in which machines (CSs) serve customers/products (EVs) based on release time, due date, deadline, and energy request, as happens in manufacturing systems.
In this thesis, first, attention is focused on defining a discrete-time optimization problem in which fossil fuel production plants, storage systems, and renewables are considered to satisfy the grid's electrical load. The discrete-time formalization can use forecasting for renewables and loads without data elaboration. On the other side, many decision variables are present, making the optimization problem hard to solve through commercial optimization tools. For this reason, an alternative method for the optimal schedule of EVs characterized by a discrete event formalization is presented. This new approach can diminish the number of variables by considering the time intervals as variables themselves. Of course, the solution's optimality is not guaranteed since some assumptions are necessary.
Moreover, the last chapter proposes a novel approach for the optimal location and line assignment for electric bus charging stations. In particular, the model provides the siting and sizing of some CSs to maintain a minimum service frequency over public transportation lines
Accurate Battery Modelling for Control Design and Economic Analysis of Lithium-ion Battery Energy Storage Systems in Smart Grid
Adoption of lithium-ion battery energy storage systems (Li-ion BESSs) as a flexible energy source (FES) has been rapid, particularly for active network management (ANM) schemes to facilitate better utilisation of inverter based renewable energy sources (RES) in power systems. However, Li-ion BESSs display highly nonlinear performance characteristics, which are based on parameters such as state of charge (SOC), temperature, depth of discharge (DOD), charge/discharge rate (C-rate), and battery-aging conditions. Therefore, it is important to include the dynamic nature of battery characteristics in the process of the design and development of battery system controllers for grid applications and for techno-economic studies analyzing the BESS economic profitability.
This thesis focuses on improving the design and development of Li-ion BESS controllers for ANM applications by utilizing accurate battery performance models based on the second-order equivalent-circuit dynamic battery modelling technique, which considers the SOC, C-rate, temperature, and aging as its performance affecting parameters. The proposed ANM scheme has been designed to control and manage the power system parameters within the limits defined by grid codes by managing the transients introduced due to the intermittence of RESs and increasing the RES penetration at the same time. The validation of the ANM scheme and the effectiveness of controllers that manage the flexibilities in the power system, which are a part of the energy management system (EMS) of ANM, has been validated with the help of simulation studies based on an existing real-life smart grid pilot in Finland, Sundom Smart Grid (SSG). The studies were performed with offline (short-term transient-stability analysis) and real-time (long-term transient analysis) simulations.
In long-term simulation studies, the effect of battery aging has also been considered as part of the Li-ion BESS controller design; thus, its impact on the overall power system operation can be analyzed. For this purpose, aging models that can determine the evolving peak power characteristics associated with aging have been established. Such aging models are included in the control loop of the Li-ion BESS controller design, which can help analyse battery aging impacts on the power system control and stability. These analyses have been validated using various use cases. Finally, the impact of battery aging on economic profitability has been studied by including battery-aging models in techno-economic studies.Aurinkosähköjärjestelmien ja tuulivoiman laajamittainen integrointi sähkövoimajärjestelmän eri jännitetasoille on lisääntynyt nopeasti. Uusiutuva energia on kuitenkin luonteeltaan vaihtelevaa, joka voi aiheuttaa nopeita muutoksia taajuudessa ja jännitteessä. Näiden vaihteluiden hallintaan tarvitaan erilaisia joustavia energiaresursseja, kuten energiavarastoja, sekä niiden tehokkaan hyödyntämisen mahdollistaviea älykkäitä ja aktiivisia hallinta- ja ohjausjärjestelmiä.
Litiumioniakkuihin pohjautuvien invertteriliitäntäisten energian varastointijärjestelmien käyttö joustoresursseina aktiiviseen verkonhallintaan niiden pätö- ja loistehon ohjauksen avulla on lisääntynyt nopeasti johtuen niiden kustannusten laskusta, modulaarisuudesta ja teknisistä ominaisuuksista. Litiumioniakuilla on erittäin epälineaariset ominaisuudet joita kuvaavat parametrit ovat esimerkiksi lataustila, lämpötila, purkaussyvyys, lataus/ purkausnopeus ja akun ikääntyminen. Akkujen ominaisuuksien dynaaminen luonne onkin tärkeää huomioida myös akkujen sähköverkkoratkaisuihin liittyvien säätöjärjestelmien kehittämisessä sekä teknis-taloudellisissa kannattavuusanalyyseissa.
Tämä väitöstutkimus keskittyy ensisijaisesti aktiiviseen verkonhallintaan käytettävien litiumioniakkujen säätöratkaisuiden parantamiseen hyödyntämällä tarkkoja, dynaamisia akun suorituskykymalleja, jotka perustuvat toisen asteen ekvivalenttipiirien akkumallinnustekniikkaan, jossa otetaan huomioon lataustila, lataus/purkausnopeus ja lämpötila. Työssä kehitetyn aktiivisen verkonhallintajärjestelmän avulla tehtävät akun pätö- ja loistehon ohjausperiaatteet on validoitu laajamittaisten simulointien avulla, esimerkiksi paikallista älyverkkopilottia Sundom Smart Gridiä simuloimalla. Simuloinnit tehtiin sekä lyhyen aikavälin offline-simulaatio-ohjelmistoilla että pitkän aikavälin simulaatioilla hyödyntäen reaaliaikasimulointilaitteistoa.
Pitkän aikavälin simulaatioissa akun ikääntymisen vaikutus otettiin huomioon litiumioniakun ohjauksen suunnittelussa jotta sen vaikutusta sähköjärjestelmän kokonaistoimintaan voitiin analysoida. Tätä tarkoitusta varten luotiin akun ikääntymismalleja, joilla on mahdollista määrittää akun huipputehon muutos sen ikääntyessä. Akun huipputehon muutos taas vaikuttaa sen hyödynnettävyyteen erilaisten pätötehon ohjaukseen perustuvien joustopalveluiden tarjoamiseen liittyen. Lisäksi väitöstutkimuksessa tarkasteltiin akkujen ikääntymisen vaikutusta niiden taloudelliseen kannattavuuteen sisällyttämällä akkujen ikääntymismalleja teknis-taloudellisiin tarkasteluihin.fi=vertaisarvioitu|en=peerReviewed
Design and simulation of converters operating in a renewable energy based smart grid
En el treball de fi de màster “Disseny i simulació d'un convertidor que funciona en una xarxa intel·ligent basada en energies renovables” s'aborda el disseny i la simulació d'un convertidor per a la seva integració en una xarxa intel·ligent basada en energies renovables. L'objectiu principal d'aquest projecte és investigar i desenvolupar dos sistemes proposats: en primer lloc, la integració d'una planta fotovoltaica en la xarxa elèctrica; i, en segon lloc, la integració d'una planta fotovoltaica juntament amb un sistema d'emmagatzematge d'energia i una càrrega de corrent altern connectat mitjançant un inversor. L'estudi se centra en la recerca i la recopilació d'informació sobre les xarxes intel·ligents basades en energies renovables, explorant els conceptes i tecnologies clau necessaris per a la integració reeixida de fonts d'energia renovable en les xarxes elèctriques. A més, es durà a terme una anàlisi exhaustiva sobre l'emmagatzematge d'energia, investigant les diferents tecnologies i sistemes d'emmagatzematge disponibles. Així mateix, s'investiga el disseny i el control de convertidors utilitzats en la formació de xarxes, analitzant els principis de funcionament i els algorismes de control necessaris per a regular la tensió i la freqüència de la xarxa. Es duu a terme una anàlisi detallada del sistema proposat, estudiant la seva configuració, els components involucrats i la seva interconnexió amb la xarxa elèctrica existent. A partir d'aquesta anàlisi, es realitza el disseny i desenvolupament dels convertidors necessaris per al sistema de generació i emmagatzematge d'energia. Es dimensionen adequadament els convertidors i es dissenyen els algorismes de control necessaris per a garantir el funcionament òptim del sistema. Per a avaluar i analitzar el rendiment del sistema proposat, s'utilitza el paquet de programari MATLAB/Simulink per al modelatge i la simulació. Això permet realitzar proves en diferents condicions i ajustar els paràmetres de disseny i control dels convertidors per a aconseguir un acompliment òptim del sistema. Els resultats obtinguts en les simulacions són validats mitjançant comparacions amb dades reals o resultats experimentals, assegurant l'eficàcia i fiabilitat del disseny proposat. Aquest treball es documenta en un informe complet i analític, que inclou una explicació detallada dels fonaments teòrics, la descripció dels passos de disseny i simulació, la presentació dels resultats obtinguts i una anàlisi crítica d'aquestsEn el trabajo de fin de máster “Diseño y simulación de un convertidor que funciona en una red inteligente basada en energías renovables” se aborda el diseño y la simulación de un convertidor para su integración en una red inteligente basada en energías renovables. El objetivo principal de este proyecto es investigar y desarrollar dos sistemas propuestos: en primer lugar, la integración de una planta fotovoltaica en la red eléctrica; y, en segundo lugar, la integración de una planta fotovoltaica junto con un sistema de almacenamiento de energía y una carga de corriente alterna conectada mediante un inversor. El estudio se centra en la investigación y la recopilación de información sobre las redes inteligentes basadas en energías renovables, explorando los conceptos y tecnologías clave necesarios para la integración exitosa de fuentes de energía renovable en las redes eléctricas. Además, se llevará a cabo un análisis exhaustivo sobre el almacenamiento de energía, investigando las diferentes tecnologías y sistemas de almacenamiento disponibles. Asimismo, se investiga el diseño y el control de convertidores utilizados en la formación de redes, analizando los principios de funcionamiento y los algoritmos de control necesarios para regular la tensión y la frecuencia de la red. Se lleva a cabo un análisis detallado del sistema propuesto, estudiando su configuración, los componentes involucrados y su interconexión con la red eléctrica existente. A partir de este análisis, se realiza el diseño y desarrollo de los convertidores necesarios para el sistema de generación y almacenamiento de energía. Se dimensionan adecuadamente los convertidores y se diseñan los algoritmos de control necesarios para garantizar el funcionamiento óptimo del sistema. Para evaluar y analizar el rendimiento del sistema propuesto, se utiliza el paquete de software MATLAB/Simulink para el modelado y la simulación. Esto permite realizar pruebas en diferentes condiciones y ajustar los parámetros de diseño y control de los convertidores para lograr un desempeño óptimo del sistema. Los resultados obtenidos en las simulaciones son validados mediante comparaciones con datos reales o resultados experimentales, asegurando la eficacia y fiabilidad del diseño propuesto. Este trabajo se documenta en un informe completo y analítico, que incluye una explicación detallada de los fundamentos teóricos, la descripción de los pasos de diseño y simulación, la presentación de los resultados obtenidos y un análisis crítico de los mismosThe Master's thesis "Design and simulation of a converter operating in a smart grid based on renewable energies" deals with the design and simulation of a converter for its integration in a smart grid based on renewable energies. The main objective of this project is to investigate and develop two proposed systems: firstly, the integration of a photovoltaic plant into the power grid; and secondly, the integration of a photovoltaic plant together with an energy storage system and an AC load connected by an inverter. The study focuses on research and information gathering on smart grids based on renewable energy, exploring the key concepts and technologies required for the successful integration of renewable energy sources into power grids. In addition, a comprehensive analysis of energy storage will be carried out, investigating the different storage technologies and systems available. The design and control of converters used in grid formation is also investigated, analyzing the operating principles and control algorithms required to regulate the voltage and frequency of the grid. A detailed analysis of the proposed system is carried out, studying its configuration, the components involved and its interconnection with the existing electrical network. Based on this analysis, the design and development of the converters required for the energy generation and storage system is carried out. The converters are adequately sized and the necessary control algorithms are designed to guarantee the optimal operation of the system. To evaluate and analyze the performance of the proposed system, the MATLAB/Simulink software package is used for modeling and simulation. This allows testing under different conditions and adjusting the design and control parameters of the converters to achieve optimal system performance. The results obtained in the simulations are validated by comparisons with real data or experimental results, ensuring the effectiveness and reliability of the proposed design. This work is documented in a comprehensive and analytical report, which includes a detailed explanation of the theoretical background, description of the design and simulation steps, presentation of the results obtained and a critical analysis of the result
Development Schemes of Electric Vehicle Charging Protocols and Implementation of Algorithms for Fast Charging under Dynamic Environments Leading towards Grid-to-Vehicle Integration
This thesis focuses on the development of electric vehicle (EV) charging protocols under a dynamic environment using artificial intelligence (AI), to achieve Vehicle-to-Grid (V2G) integration and promote automobile electrification. The proposed framework comprises three major complementary steps. Firstly, the DC fast charging scheme is developed under different ambient conditions such as temperature and relative humidity. Subsequently, the transient performance of the controller is improved while implementing the proposed DC fast charging scheme. Finally, various novel techno-economic scenarios and case studies are proposed to integrate EVs with the utility grid.
The proposed novel scheme is composed of hierarchical stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process is implemented using the constant current-constant voltage (CC-CV) protocol. Where the relative humidity impact on the charging process was not investigated or mentioned in the literature survey. This was followed by the feedforward backpropagation neural network (FFBP-NN) classification algorithm supported by the statistical analysis of an instant charging current sample of only 10 seconds at any ambient condition. Then the FFBP-NN perfectly estimated the EV’s battery terminal voltage, charging current, and charging interval time with an error of 1% at the corresponding temperature and relative humidity. Then, a nonlinear identification model of the lithium-polymer ion battery dynamic behaviour is introduced based on the Hammerstein-Wiener (HW) model with an experimental error of 1.1876%.
Compared with the CC-CV fast charging protocol, intelligent novel techniques based on the multistage charging current protocol (MSCC) are proposed using the Cuckoo optimization
algorithm (COA). COA is applied to the Hierarchical technique (HT) and the Conditional random technique (CRT). Compared with the CC-CV charging protocol, an improvement in the charging efficiency of 8% and 14.1% was obtained by the HT and the CRT, respectively, in addition to a reduction in energy losses of 7.783% and 10.408% and a reduction in charging interval time of 18.1% and 22.45%, respectively. The stated charging protocols have been implemented throughout a smart charger. The charger comprises a DC-DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory neural network (LSTM). The LSTM network model was
utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. The NNPC–LSTM controller was compared with the fuzzy logic (FL) and the
conventional PID controllers and perfectly ensured that the optimum transient performance with a minimum battery terminal voltage ripple reached 1 mV with a very high-speed response
of 1 ms in reaching the predetermined charging current stages.
Finally, to alleviate the power demand pressure of the proposed EV charging framework on the utility grid, a novel smart techno-economic operation of an electric vehicle charging station (EVCS) in Egypt controlled by the aggregator is suggested based on a hierarchical model of multiple scenarios. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station. Mixed-integer linear programming (MILP) is used to solve the first stage, where the EV charging peak demand value is reduced by 3.31% (4.5 kW). The second challenging stage is to maximize the EVCS profit whilst minimizing the EV charging tariff. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted in an increase in EVCS revenue by
28.88% and 20.10%, respectively. Furthermore, the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are applied to the stochastic EV parking across the day, controlled by the aggregator to alleviate the utility grid load demand. The aggregator determined the number of EVs that would participate in the electric power trade and sets the charging/discharging capacity level for each EV. The proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner and minimizing the utility grid load demand based on the genetic algorithm (GA). The implemented procedure reduced the degradation cost by an average of 40.9256%, increased the EV SOC by 27%, and ensured an effective grid stabilization service by shaving the load demand to reach a predetermined grid average power across the day where the grid load demand decreased by 26.5% (371 kW)
Optimal integration of wind energy with a renewable based microgrid for industrial applications.
Wind energy in urban environments is a rapidly developing technology influenced by the terrain specifications, local wind characteristics and urban environments such as buildings architecture. The urban terrain is more complex than for open spaces and has a critical influence on wind flow at the studied site. This approach proposes an integration of the surrounding buildings in the studied site and then simulating the wind flow, considering both simple and advanced turbulence models to quantify and simulate the wind flow fields in an urban environment and evaluate the potential wind energy. These simulations are conducted with an accessible computational fluid dynamic tool (Windsim) implementing available commercial wind turbines and performed on a case study at Agder county in the southern part of Norway for an industrial facility specialized in food production. Several simulations were considered and repeated to achieve a convergence after adding the buildings to the domain, which mainly simulates the wind flow patterns, power density, and annual energy production. These simulations will be compared with previous results, which adapted different manipulation techniques applied on the same site where the elevation and roughness data were manipulated to mimic the actual conditions in the studied urban site. The current approach (adding the buildings) showed a reduction in the average wind speed and annual energy production for certain levels with increased turbulence intensity surrounding the buildings. Moreover, a feasibility study is conducted to analyze the techno-economic of the facility's hybrid system, including the planned installation of a wind energy system using commercial software (HOMER). The simulation results indicated that HOMER is conservative in estimating the annual energy production of both wind and solar power systems. Nevertheless, the analysis showed that integrating a wind turbine of 600 kW would significantly reduce the dependence on the grid and transform the facility into a prosumer with more than 1.6 GWh traded with the grid annually. However, the proposed system's net present cost would be 1.43 M USD based on installation, maintenance, and trading with the grid, without including self-consumption, which counts for approximately 1.5 GWh annually. Moreover, the proposed system has a low levelized cost of energy of 0.039$ per kWh, which is slightly above the levelized cost of wind energy but 2 to 4 times less than the installed solar panels
Development of optimal energy management and sizing strategies for large-scale electrical storage systems supporting renewable energy sources.
284 p.El desarrollo e integración de las fuentes de energía renovable (RES) conducirá a un futuro energético más sostenible. Las plantas renovables deberán mejorar su participación y operación a través de los mercados de electricidad de una manera más controlada y segura. Además, el diseño actual del mercado está cambiando para permitir una participación inclusiva en mercados de flexibilidad. En este contexto, los sistemas de almacenamiento de energía (ESS) se consideran una de las tecnologías flexibles clave que pueden apoyar la operación de las energías renovables, mediante servicios como: 1) control de la potencia generada, 2) mejora de los errores de predicción, y 3) provisión de servicios auxiliares de regulación de frecuencia. Sin embargo, el desarrollo del almacenamiento ha sido frenado también por sus altos costos. Por lo tanto, esta tesis doctoral aborda el tema del ¿Desarrollo de estrategias óptimas de gestión y dimensionamiento de los sistemas de almacenamiento eléctrico a gran escala como apoyo a fuentes de energía renovable¿, con el objetivo de desarrollar una metodología con una perspectiva global, mediante una estrategia de gestión de energía avanzada (EMS) que aborda la gestión de activos (RES + ESS) a largo plazo y por otro lado, el cálculo del dimensionamiento y operación del almacenamiento a corto plazo (en la operación en tiempo real), para asegurar un marco adecuado que permita evaluar la rentabilidad de la integración del almacenamiento en aplicaciones conectadas a la red. La estrategia de gestión de energía propuesta es validada a través de dos casos de estudio: una planta renovable individual (eólica o solar) con almacenamiento, y un porfolio de renovables y almacenamiento
Development of optimal energy management and sizing strategies for large-scale electrical storage systems supporting renewable energy sources.
284 p.El desarrollo e integración de las fuentes de energía renovable (RES) conducirá a un futuro energético más sostenible. Las plantas renovables deberán mejorar su participación y operación a través de los mercados de electricidad de una manera más controlada y segura. Además, el diseño actual del mercado está cambiando para permitir una participación inclusiva en mercados de flexibilidad. En este contexto, los sistemas de almacenamiento de energía (ESS) se consideran una de las tecnologías flexibles clave que pueden apoyar la operación de las energías renovables, mediante servicios como: 1) control de la potencia generada, 2) mejora de los errores de predicción, y 3) provisión de servicios auxiliares de regulación de frecuencia. Sin embargo, el desarrollo del almacenamiento ha sido frenado también por sus altos costos. Por lo tanto, esta tesis doctoral aborda el tema del ¿Desarrollo de estrategias óptimas de gestión y dimensionamiento de los sistemas de almacenamiento eléctrico a gran escala como apoyo a fuentes de energía renovable¿, con el objetivo de desarrollar una metodología con una perspectiva global, mediante una estrategia de gestión de energía avanzada (EMS) que aborda la gestión de activos (RES + ESS) a largo plazo y por otro lado, el cálculo del dimensionamiento y operación del almacenamiento a corto plazo (en la operación en tiempo real), para asegurar un marco adecuado que permita evaluar la rentabilidad de la integración del almacenamiento en aplicaciones conectadas a la red. La estrategia de gestión de energía propuesta es validada a través de dos casos de estudio: una planta renovable individual (eólica o solar) con almacenamiento, y un porfolio de renovables y almacenamiento
Towards the next generation of smart grids: semantic and holonic multi-agent management of distributed energy resources
The energy landscape is experiencing accelerating change; centralized energy systems are being decarbonized, and transitioning towards distributed energy systems, facilitated by advances in power system management and information and communication technologies. This paper elaborates on these generations of energy systems by critically reviewing relevant authoritative literature. This includes a discussion of modern concepts such as ‘smart grid’, ‘microgrid’, ‘virtual power plant’ and ‘multi-energy system’, and the relationships between them, as well as the trends towards distributed intelligence and interoperability. Each of these emerging urban energy concepts holds merit when applied within a centralized grid paradigm, but very little research applies these approaches within the emerging energy landscape typified by a high penetration of distributed energy resources, prosumers (consumers and producers), interoperability, and big data. Given the ongoing boom in these fields, this will lead to new challenges and opportunities as the status-quo of energy systems changes dramatically. We argue that a new generation of holonic energy systems is required to orchestrate the interplay between these dense, diverse and distributed energy components. The paper therefore contributes a description of holonic energy systems and the implicit research required towards sustainability and resilience in the imminent energy landscape. This promotes the systemic features of autonomy, belonging, connectivity, diversity and emergence, and balances global and local system objectives, through adaptive control topologies and demand responsive energy management. Future research avenues are identified to support this transition regarding interoperability, secure distributed control and a system of systems approach