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With an increase in the electrification of end-use sectors, various resources on the demand side provide great flexibility potential for system operation, which also leads to problems such as the strong randomness of power consumption behavior, the low utilization rate of flexible resources, and difficulties in cost recovery. With the core idea of 'access over ownership', the concept of the sharing economy has gained substantial popularity in the local energy market in recent years. Thus, we provide an overview of the potential market design for the sharing economy in local energy markets (LEMs) and conduct a detailed review of research related to local energy sharing, enabling technologies, and potential practices. This paper can provide a useful reference and insights for the activation of demand-side flexibility potential. Hopefully, this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.

Funding Information: This research has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement (EnergyMatching project number 768766), and J. Gust. Richert foundation in Sweden (grant number: 2020-00586). Funding Information: This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement (EnergyMatching project number 768766 ), and J. Gust. Richert foundation in Sweden (grant number: 2020-00586 ). Publisher Copyright: © 2021 The Author(s) Peer-to-peer (P2P) energy sharing among neighboring households is a promising solution to mitigating the difficulties of renewable power (such as solar Photovoltaics (PV)) penetration on the power grid. Until now, there is still a lack of study on the impacts of future climate change on the P2P energy trading performances. The future climate change will cause variances in the renewable energy production and further lead to changes in the economic performances of households with various energy uses and affect the decision making in PV ownership and pricing strategies. Being unaware of these impacts could potentially hinder the P2P energy sharing application in practice. To bridge such knowledge gap, this paper conducts a systematic investigation of the climate change impacts on the energy sharing performance in solar PV power shared communities. The future weather data is generated using the Morphine method, and an agent-based modeling method is used for simulating the energy trading behaviors of households. Four comparative scenarios of different PV ownerships and pricing strategies are designed. The detailed energy trading performances (including the PV power self-sufficiency, cost saving, revenues, and compound annual growth rate) for the four comparative scenarios are analyzed under both the present and future climates and compared. The study results of a building community located in Sweden show that the future climate change is more beneficial to large energy use households while less beneficial to small households. High price of energy trading can improve the fairness of the economic performances in the community, especially when some of the households do not have any PV ownership. This study can help understand the future climate impacts on the energy sharing performances of building communities, which can in turn guide decision making in PV ownership and price setting for different households under the future climate change to facilitate real applications.

In Europe, the recast of Directive 2018/2001 defined Renewable Energy Communities as innovative configurations for renewable energy sharing between different end user types. In this regard, this work aims to assess the benefits following the constitution of a Renewable Energy Community in the industrial area of Benevento (South of Italy), involving a mixed-use building and an industrial wastewater treatment plant. The alternative single end users’ configuration has been also examined, and both solutions have been compared with the current state where the users’ electric energy requests are fully met by the power grid. The users have been equipped with a 466 kWp photovoltaic plant, modelled in HOMER Pro®, providing in input experimental meteorological data (global solar radiation and air temperature) collected by one of the weather control units in Benevento. Real data about users’ electric energy demand have been gathered from their electricity bills, and when unavailable their electric load profiles on an hourly basis have been reconstructed based on the aggregated monthly data. Energy sharing has been proven to increase energy self-consumption and the users’ self-sufficiency. Annually, the primary energy demand is reduced by 577 MWh (1.2 MWh/kWp), carbon dioxide emissions by 84 tCO2 and operative costs by 101 kEUR.

For the purpose of enabling long-term autonomy of a heterogeneous swarm of marine robots, task allocation and sequencing were introduced into the system’s energy management procedures and agent interactions. In a scenario where the system needs to autonomously go about its monitoring mission and survive long-term, the available maximum capacity of five surface vehicles - aPad platforms which represent the charging hubs of the system - is usually outnumbered by the number of active charging requests by the sensor node-like aMussel agents, leading to a need for careful planning and optimisation of robot activities. In the scope of this thesis, a two-layered system of decision-making algorithms was developed: a low-level specific solution-focused set of algorithms, and a high-level hyper-heuristic which selects and seamlessly switches between them, evaluates performance achieved in each step of agent interaction in the monitoring mission, and employs reinforcement learning to enable a level of adaptation to unknown environments, environmental changes, or changes to the agents themselves such as thruster failure. Performance indices were defined to appropriately represent system capabilities and achieved states, primarily related to energy-efficient movement, preserving quality coverage of the monitoring area, and collecting the maximum amount of sensor measurements. Finally, a method of scoring and ranking the performance of the various decision-making methods present in the system was defined, as well as a benchmark to enable validation during a variety of relevant scenarios. These metrics were applied during the analysis of both simulated and field experiment results. U svrhu omogućavanja dugoročne autonomije heterogenog roja morskih robota, u postupke upravljanja energijom sustava i interakcije među agentima uvode se algoritmi raspoređivanja zadataka. U scenariju u kojem višerobotski sustav treba samostalno provoditi dugoročnu nadzornu misiju, raspoloživi maksimalni kapacitet od pet autonomnih površinskih vozila - aPad platformi koje predstavljaju punjače sustava - je obično nadmašen brojem aktivnih zahtjeva za punjenjem, što dovodi do potrebe za pažljivim planiranjem i optimizacijom robotskih aktivnosti. U okviru ovog istraživanja razvija se dvoslojni sustav algoritama za donošenje odluka: na nižoj razini je niz algoritama baziranih na različitim paradigmama strojnog učenja usmjerenih na specifične situacije i rješenja dok je na višoj razini hiperheuristički algoritam koji odabire između njih. Predloženi predmet istraživanja i primjene je višeslojno društvo podvodnih i pomorskih robota razvijeno kao dio Horizon 2020 FET projekta subCULTron. Cilj ovog istraživanja je postizanje dugoročne autonomije u učećem, samoregulirajućem i samoodrživom roju pomorskih robota koji izvršava nadzornu i istraživačku misiju u izazovnom pomorskom okruženju venecijanske lagune. Heterogeni robotski sustav sastoji se od tri zasebne vrste agenata, od kojih su ovdje dva najznačajnija umjetne školjke (aMussels) koje putuju između morskog dna (gdje djeluju kao senzorske jedinice) i površine, te umjetni lopoči (aPads) na površini vode koji omogućuju razmjenu informacije i energije, a sadrže i mehaničke priključne stanice za punjenje baterija aMussel robota. aPad je preaktuirana površinska platforma opremljena s četiri potisnika, što mu daje značajnu slobodu gibanja, dok aMussel nema aktuatora osim sustava za upravljanje uzgonom. Komunikacija je vrlo važna u distribuiranoj strukturi roja, što znači da se tijekom rada moraju poštovati ograničenja koja se temelje na dometu dvaju dostupnih primarnih načina komunikacije (WiFi i akustički signal). Nadalje, roj mora biti sposoban prilagoditi se promjenama u okolini i stvarnim morskim uvjetima, kako borbom protiv tako i iskorištavanjem utjecaja pojava kao što su morske struje i vjetar. Naglasak na nenadziranom radu roja i prilagodbi promjenama okoline sugerira primjenu metoda strojnog učenja. Područje umjetne inteligencije može se podijeliti prema mnogim kriterijima, no pet paradigmi strojnog učenja uključuju: • spojne metode (neuronske mreže) • genetske algoritme i klasifikatore • empirijske metode za stvaranje pravila i stabla odlučivanja • analitičke metode učenja • pristupe na temelju slučajeva Predložena struktura sustava za donošenje odluka ima dva glavna sloja: na nižoj razini nalazi se skup algoritama (heuristika) za dodjelu zadataka temeljenih na aspektima nekoliko od gore navedenih paradigmi strojnog učenja, dok algoritam na višoj razini (hiperheuristika) odabire između njih. Hiperheuristika je općenito definirana kao automatizirana metodologija za odabir ili generiranje heuristike za rješavanje teških računskih problema pretraživanja. Izvorno nazvana "heuristika za odabir heuristika", ona predstavlja pristup na visokoj razini koji može odabrati i na konkretan problem primijeniti odgovarajuću heuristiku na nižoj razini, te to učiniti u svakoj točki odlučivanja. Heurističke i metaheurističke metode uspješne su u rješavanju problema pretraživanja u stvarnom svijetu, međutim nailaze na poteškoće u primjeni na nove probleme ili čak na nove slučajeve vrlo sličnih problema. Glavni uzrok tih poteškoća je širok raspon algoritama i parametara koji su uključeni u rješavanje problema, kao i nedostatak smjernica o tome kako odabrati između njih i kako ih podesiti. Osim toga, razina razumijevanja zašto pojedine heuristike rade u određenim situacijama, a ne u drugima često nije dostatna za jednostavno donošenje izbora, a teškoće u preciznom modeliranju problema i realnih situacija znače da strogo matematički optimalna rješenja možda zapravo i nisu najbolja moguća rješenja u primjeni. Cilj korištenja hiperheuristike jest povećanje razine općenitosti na kojoj optimizacijski sustavi mogu djelovati. Doktorski rad podijeljen je na uvodni dio ("1. Introduction") u kojem je dan opis glavne motivacije iza teme istraživanja – autonomnog dugoročnog nadzora izazovnih pomorskih ekosustava – i uključuje kratki pregled istraživanja umjetnog života i robotskih društava, s naglaskom na evolucijske algoritme i učenje potkrepljenjem. Poglavlje završava pregledom strukture rada, temeljnih hipoteza i izvornog znanstvenog doprinosa. Sam robotski roj korišten u radu opisan je u drugom poglavlju ("2. Heterogeneous marine swarm agents and interactions"). Poglavlje sadrži pregled robotskih agenata koji čine heterogeni pomorski roj s naglaskom na agente aPad i aMussel, opisujući hardverska i softverska rješenja koja su razvijena i korištena kako bi se ispunili svi preduvjeti za cilj nenadzirane dugoročne misije praćenja i istraživanja okoliša, uključujući modeliranje agenata koje omogućava testiranje sustava u simulacijama i eksperimentima sa stvarnim vozilima. Poglavlje također opisuje interakcije agenata s ciljem povećanja autonomije roja, posebno naglašavajući kako je postignuta razmjena energije između agenata, zaključno s eksperimentalnom validacijom razvijenih algoritama za autonomno međusobno sakupljanje i punjenje agenata. Treće poglavlje ("3. Multi-robot task assignment and low-level heuristics") fokusira se na specifični problem raspoređivanja interakcija i zadataka među agentima koji je glavni predmet rada, kao i na razvijenu strukturu sustava za donošenje odluka. Detaljno se opisuju metode podjele i klasteriranja koje se koriste za dodjeljivanje početnih područja odgovornosti agentima i niža razina sustava donošenja odluka koja se sastoji od situacijskih heuristika koje uključuju znanje o okolini. Poglavlje opisuje i pokazatelje učinkovitosti odabrane za evaluaciju željenih aspekata i mogućnosti sustava. Opisane su i rane simulacije robotskog roja temeljene na diskretnim događajima, kao i početni eksperimenti za validaciju komunikacije i metode postizanja konsenzusa među agentima u roju. U svrhu osmišljavanja heuristika niže razine, problem dodjeljivanja zadataka aPadima može se opisati kao vrsta problema usmjeravanja vozila. Heurističke metode rješavanja koje su usmjerene na specifične varijante problema usmjeravanja vozila, kao što su problem usmjeravanja vozila s vremenskim prozorima, kapacitirani problem usmjeravanja vozila, problem usmjeravanja vozila s više opskrbnih mjesta ili problem usmjeravanja vozila sa stohastičkim zahtjevima, intenzivno su proučavane, uz razvoj, implementaciju i detaljno prilagođavanje heuristika kako bi odgovarale određenoj vrsti problema. Budući da se obilježja problema mogu znatno razlikovati, nije nužno uvijek sasvim jasno koja će metoda dati najbolje rješenje za određeni slučaj problema. Ovdje je cilj osmisliti nekoliko kontekstualno specifičnih heuristika s dobrim ponašanjem u određenoj situaciji, a zatim odabirati između njih i kombinirati pristupe prema potrebi. Neki od pristupa uključuju korak odvajanja aMussel robota u grupe ili klastere koji odgovaraju parametrima kao što su ukupna raspoloživa energija, snaga signala ili izvediva udaljenost kretanja, a zatim dodjeljivanje aPadova pojedinačnim klasterima kao oblik određivanja "područja interesa" kako bi se osigurala pokrivenost i u smislu komunikacije i u smislu punjenja. Klasteriranje je problem prisutan u rudarenju podataka, bazama podataka, kompresiji podataka i strojnom učenju. Ono uključuje podjelu skupova opažanja u klastere tako da su intra-klasterska opažanja što je moguće sličnija (ili bliža, u odabranoj metrici), a inter-klasterska opažanja što različitija (ili dalja). Drugi je cilj klasteriranja smanjiti složenost podataka zamjenom skupine opažanja jednim reprezentativnim opažanjem, što dovodi do lakšeg i bržeg računanja i analize. Algoritmi bazirani na particioniranju, kao što je široko korišteni k-means algoritam, su specifični podtip klasteriranja koji organizira objekte u određeni broj particija, gdje svaka od njih predstavlja jedan klaster. Klasteri se formiraju na temelju funkcije udaljenosti, što dovodi do formiranja samo sfernih klastera i dozvoljava utjecaj šuma na rezultate klasteriranja. Ako se razmatra specifičan problem dodjele zadataka za komunikacijsko pokrivanje i punjenje robota, ovo svojstvo nije osobito problematično, jer su konveksni i sferni klasteri zaista željeni rezultat. K-means algoritam zahtijeva da broj klastera bude unaprijed poznat i zadan, ali se inače može smatrati potpuno nenadziranim ili, u slučaju uključivanja nekih prethodno poznatih saznanja o željenim ishodima, djelomično nenadziranim. Diferencijalna evolucija je još jedan predstavnik heurističkih pristupa niže razine dodjele zadataka i odlučivanja unutar roja. Izvorno je predložena kao iterativna heuristika za globalnu optimizaciju u kontinuiranom prostoru temeljena na populaciji, a kasnije je prilagođena za upotrebu u diskretnim prostorima i u problemima sekvenciranja, permutacije i raspoređivanja, uključujući i problem usmjeravanja vozila. Uvođenjem specifičnog kodiranja gena i populacije i stvaranjem prikladnih funkcija troška, kazni i uvjeta zaustavljanja, moguće je predstaviti i uvesti ograničenja u prostoru rješenja. U ovom će se slučaju kriteriji podudarati s čimbenicima iz stvarnog svijeta kao što su snaga morske struje i razina napona baterije robota, s genima koji kodiraju nizove zadataka za pojedina vozila. Razvoj prvog sloja sustava za donošenje odluka uključuje razvoj skupa algoritama za dodjelu zadataka koji koriste ili kombiniraju različite paradigme strojnog učenja ili ih koriste na različite načine, s ciljem osmišljavanja rasporeda kretanja i plana izvršavanja zadataka za dostupna vozila, prvenstveno u smislu punjenja ili premještanja drugih robota unutar roja. Ovo uključuje metodu klasteriranja, metodu klasteriranja s ograničenjima, metodu diferencijalne evolucije i hibridne metode. Osim toga, prisutne su vrlo jednostavne i situacijske heuristike razvijene tijekom rada s robotskim sustavom i koje odgovaraju na specifične probleme koji su uočeni. To su Greedy heuristika, koja u svakom koraku odabire najbližu mušulu kako bi se minimiziralo kretanje aPada; Cautious heuristika, koja u svakom koraku bira mušulu s najpraznijom baterijom kojoj je hitno potrebno punjenje; Rescue heuristika, koja bira mušulu najudaljeniju od centroida klastera kako bi se izbjegao gubitak robota i očuvali outlieri roja, te Rush heuristika, koja bira mušulu s najviše baterije, a koja je još uvijek kandidat za punjenje, kako bi se minimiziralo gubljenje korisnog vremena rada mušula. Posebna se pozornost posvećuje rubnim slučajevima u kojima se pojedine metode u određenim uvjetima ponašaju veoma dobro, a u drugima veoma loše (uključujući i uopće ne ostvarivanje konvergencije na valjano rješenje) jer će to biti vrijedan budući pokazatelj uspješnosti za hiperheuristički dio biranja algoritama. Drugi sloj sadrži hiperheuristički algoritam koji odabire između heuristika implementiranih u donjem sloju ovisno o situaciji u okolini i unutar roja, s krajnjim ciljem autonomnog i nenadziranog planiranja zadataka i istovremenog optimiranja potrošnje i distribucije energije. Pokazatelji učinkovitosti kojima se ocjenjuje rad sustava uključuju broj aktivnih i prisutnih agenata, prostornu raspodjelu agenata unutar roja, te praćenje ukupne količine vremena koje su agenti proveli radeći "koristan" posao, kao i raspodjelu tog vremena. Viša razina sustava odlučivanja - drugi sloj - opisana je u četvrtom poglavlju ("4. High-level heuristics - hyper-heuristics"). Poglavlje počinje pregledom hiperheuristike u literaturi, uključujući klasifikaciju i općeprihvaćene oblike izvedbe. Zatim se opisuje odabrana i implementirana viša razina strukture odlučivanja, uključujući detaljni opis metode odabira, donošenja odluka i učenja potkrepljenjem koji se koriste kako bi se sustavu dala prilagodljivost i autonomija. Definirane su metode vrednovanja rješenja koje koriste bodovanje i rangiranje, a u poglavlju su opisane i analizirane i simulirane i eksperimentalne varijante validacijskog referentnog scenarija. Hiperheuristike se dijele na tri kategorije ovisno o tome uče li tijekom pretraživanja ili prije pretraživanja, ili uopće ne dobivaju povratne informacije iz prostora pretraživanja: hiperheuristika s on-line učenjem, s off-line učenjem i bez učenja. Mogu biti klasificirane i kao selekcijske ili generacijske hiperheuristike, ovisno o tome odabiru li heuristiku iz skupa postojećih heuristika ili generiraju nove heuristike iz komponenti postojećih heuristika niže razine. Hiperheuristika korištena u ovom radu je selekcijska hiperheuristika s online učenjem. Selekcijska hiperheuristika bavi se problemima neizravno, pregledavanjem skupa dostupnih heuristika za svaki korak pretraživanja i odabirom one metode koju će se primijeniti na problem na temelju statistike prethodnih performansi svih metoda prema danom skupu metrika i pokazatelja. Strategija odabira ruletom često se koristi u evolucijskim algoritmima. Primijenjeno na hiperheuristički okvir, rulet odabire heuristiku s vjerojatnošću proporcionalnom njezinoj sposobnosti. Modificiranjem parametara sposobnosti metoda na temelju povratnih informacija primljenih iz prostora problema korištenjem više pokazatelja učinkovitosti, online učenje s potkrepljenjem uvodi se u sustav donošenja odluka. Nakon svake primjene heuristike niže razine, ona se ocjenjuje na temelju odabranih pokazatelja. Ako je heuristika imala bolju učinkovitost od dotadašnjeg prosjeka s obzirom na određeni pokazatelj, ona se pozitivno ocjenjuje za taj pokazatelj i njezina se sposobnost povećava za svaki takav "uspjeh". U suprotnom, ako heuristika ima lošiju učinkovitost, smatra se da nije uspjela i dobiva fiksnu "kaznu" koja umanjuje njezinu sposobnost i time joj smanjuje vjerojatnost ponovnog odabira. Hiperheuristička struktura znači rad odvojen od problemske domene, sa svim aspektima stvarnog svijeta apstrahiranim u pokazatelje koji se nakon implementacije i evaluacije vraćaju hiperheuristici. Ako, na primjer, tijekom eksperimenta struja, vjetar ili kvar na vozilu otežaju aPadu da se pomakne i dosegne aMussele odabrane u predloženom rješenju, trošak kretanja bit će znatno veći nego ranije u eksperimentu, a heuristika niže razine koja je proizvela ovo rješenje bit će kažnjena, dok će ona metoda koja predloži rješenje koje može na bilo koji način zaobići ili kompenzirati ovu smetnju i smanjiti troškove kretanja biti nagrađena, čime se postiže postupna adaptacija čitavog sustava. Zbog prirode sustava na koji se primjenjuje, učenje s potkrepljenjem mora biti u stanju donositi zaključke i odgovarajuće prilagodbe sposobnosti na temelju relativno oskudnih i rijetkih povratnih informacija. Budući da optimalno rješenje ovdje nije poznato i računalno ga je previše zahtjevno pronaći, kako bi se ocijenila izvedba implementirane hiperheuristike, usporedbe se rade pomoću najboljeg postignutog rješenja. Hiperheuristički odabir s učenjem s potkrepljenjem trebao bi biti bolji od jednostavnog odabira jedne od heuristika i njezine kontinuirane upotrebe ili nasumične primjene dostupnih heuristika. Učinkovitost se procjenjuje na temelju četiri odabrana pokazatelja: vrijeme rada aMussel robota, troškovi kretanja aPada, očuvanje outliera među aMussel robotima i (ne)uravnoteženost distribucije rada aMussela. U slučaju neravnoteže troškova kretanja i vremena rada, niža ocjena je bolja (što implicira da su kretanje i potrošnja energije aPada minimizirani, a koristan rad i vrijeme rada bili su ravnomjernije raspoređeni među svim agentima), dok je viša ocjena bolja za očuvanje outliera i postignuto ukupno vrijeme rada aMussela (što znači da su mjerenja konzistentno dobivana i od udaljenih agenata). Različiti eksperimenti ocjenjuju se korištenjem metoda rangiranja. Metoda koja pronalazi najbolje rješenje u skupu eksperimenata koji se ocjenjuju dobiva najnižu vrijednost, dok metoda s najlošijom izvedbom dobiva najveću vrijednost, a sve ostale se nalaze između. Metoda s ukupnim najnižim rangom stoga se može smatrati metodom s najboljim ostvarenim uspjehom. Tako su predstavljena i vrednovana četiri simulirana eksperimenta/scenarija: osnovna simulacija roja bez smetnji ili ekstrema, dvije instance u kojima se smetnja javlja u tijeku eksperimenta što dovodi do promjene u sposobnostima agenata, a time i ponašanja cjelokupnog roja, i jedna instanca u kojoj postoji jedan aMussel agent postavljen na znatnoj udaljenosti od ostatka roja. Eksperimentalni scenarij ekvivalentan osnovnoj simulaciji bez smetnji izveden je i sa stvarnim aPad vozilom. Rezultati simulacija i eksperimenata potvrđuju hipotezu da je moguće kontinuirano generirati nizove jednostavnih i računski nezahtjevnih heuristika koje su po definiranim pokazateljima učinkovitosti uspješnije od opetovane primjene svake pojedinačne heuristike, kao i njihove nasumične primjene. Kao hiperheuristička struktura s čvrsto postavljenom domenskom barijerom, sustav može generalizirati na različite nove situacije koje se pojavljuju u problemskom prostoru, npr. promjene u vrstama, broju i mogućnostima agenata roja, promjene u uvjetima u okolini roja i rad u različitim okolinama općenito. Temeljem upravljačkih algoritama i metodologija za validaciju algoritama razvijenih unutar doktorata izdvojena su tri znanstvena doprinosa: 1. Metoda dodjele i nizanja zadataka za više robota koji osiguravaju dugoročnu autonomiju heterogenog roja pomorskih robota uzimajući u obzir ograničenja okoline. 2. Hiperheuristička metoda za donošenje odluka unutar heterogenog roja pomorskih robota temeljena na nenadziranom odabiru između metoda dodjele i nizanja zadataka. 3. Metoda vrednovanja rješenja i definicija pokazatelja učinkovitosti i referentnog scenarija za procjenu valjanosti metoda donošenja odluka i dodjele zadataka primijenjenih na heterogenom roju pomorskih robota. Doktorski rad završava pregledom hipoteza i gore navedenih doprinosa te sažetkom najvažnijih točaka disertacije. Na temelju prezentiranog sadržaja ponovno se postavljaju te detaljnije razrađuju hipoteze i kao dokaz inovativnosti istraživanja nudi se popis publiciranih znanstvenih radova.

The proliferation of electric vehicles (EVs), owing to their advantages over internal combustion engine vehicles, has introduced many challenges, due to the lack of charging infrastructure that can handle such a large surge of EVs. Therefore, alternative feasible charging solutions, such as EV-to-Grid (V2G) and EV-to-EV (V2V) charging have gained prominence thanks to the bidirectional charger. However, there are several challenges hindering the adoption of V2V energy sharing solutions. The existing frameworks that detail the main aspects in energy management protocols emphasize solely on the EV integration with the grid, with the assumption of the grid availability and capability of supporting V2V energy sharing. In this paper, a novel holistic energy management framework for an efficient V2V energy sharing is proposed. The proposed framework offers a complete overview of the different stages in the V2V charging problem and introduces possible solutions for each stage and its integration with other stages to form a comprehensive V2V solution, that is not only cost-effective but also maximizes user satisfaction and social welfare, while simultaneously fulfills the highest number of energy demands.

In this article, we propose an economic storage sharing framework for prosumers and energy storage providers (ESPs) to promote renewable energy utilization cooperatively. The optimal shared capacities of ESPs and the energy sharing profiles of prosumers are first derived via minimizing social energy costs. Then the storage sharing profits of ESPs and the energy sharing payments of prosumers are successively determined by the asymmetric bargaining-based benefits sharing model. Specifically, the prosumer group bargains with the ESPs with the nominal required capacity and the shared capacities as their bargaining power to share the storage sharing benefits. Then prosumers bargain with each other to share the energy sharing benefits with their bargaining power quantified by a nonlinear energy sharing mapping method. Therefore, the benefits sharing model based on the contributions of prosumers and ESPs is fair enough for the participants. Numerical simulation tests verify the efficiency of the proposed framework.

Citizens are set to play an active role in the energy transition by transforming from ‘passive’ consumers to ‘active’ prosumers. Renewable Energy Communities (RECs) are envisioned as modes of collective prosumership by citizens under the Renewable Energy Directive of 2018 (RED II). A holistic understanding of RECs is essential to identify the benefits and challenges of collective prosumership. RECs have been the topic of several modelling studies, but a single model that simulates RECs from an integrated perspective—combining technical, economic and ecological analysis—is absent. Wide variability in the indicators discourages comparison of the results across studies. This article builds on the existing knowledge by proposing an integrated model to undertake a multi-disciplinary assessment of a potential REC. First, the proposed model analyses the technical possibilities of collective prosumership using energy flow analysis based on consumption and generation profiles. Second, the model evaluates the economic impacts of prosumership from two perspectives: from the consumers’ perspective (in terms of the annual cost of energy consumption) and from an investor’s perspective (in terms of the net present value of the investment). Thirdly, the model quantifies the annual greenhouse gas emissions of energy consumption (expressed in CO2 equivalent) to evaluate the ecological impact of prosumership. Lastly, a set of key performance indicators (KPIs) are proposed that can be used to interpret and compare the results of simulations and are mapped to the actors in the REC in line with their objectives. The proposed approach offers a single, replicable model that can be used to simulate RECs in the different Member States of the European Union. The KPIs can be used to compare the impact of combinations of various prosumership activities within the same REC or to compare two different RECs on the benefits offered vis-a-vis the investments incurred. The KPIs also offer insights into the aligning and conflicting objectives of the stakeholders of the REC.

With the proliferation of distributed generators and energy storage systems, traditional passive consumers in power systems have been gradually evolving into the so-called “prosumers”, i.e., proactive consumers, which can both produce and consume power. To encourage energy exchange among prosumers, energy sharing is increasingly adopted, which is usually formulated as a generalized Nash game (GNG). In this paper, a distributed approach is proposed to seek the Generalized Nash equilibrium (GNE) of the energy sharing game. To this end, we first prove the strong monotonicity of the game. Then, the GNG is converted into an equivalent optimization problem. An algorithm based on Nesterov’s methods is thereby devised to solve the equivalent problem and consequently find the GNE in a distributed manner. The convergence of the proposed algorithm is proved rigorously based on the nonexpansive operator theory. The performance of the algorithm is validated by experiments with three prosumers, and the scalability is tested by simulations using 1888 prosumers.

This report from the REScoopVPP project focuses on the regulation of energy sharing in 9 different European countries and the UK. It provides a systematic assessment of the existing framework according to 5 perspectives: (1) Key aspects of energy sharing regulation, (2) Allocation of roles, (3) Technical rules for energy sharing, (4) Economics and (5) Availability of information and facilitation.The report concludes with a series of 19 recommendations and takeaways to improve the regulation of existing and future national regulations.

Residential solar installations are becoming increasingly popular among homeowners. However, renters and homeowners living in shared buildings cannot go solar as they do not own the shared spaces. Community-owned solar arrays and energy storage have emerged as a solution, which enables ownership even when they do not own the property or roof. However, such community-owned systems do not allow individuals to control their share for optimizing a home’s electricity bill. To overcome this limitation, inspired by the concept of virtualization in operating systems, we propose virtual community-owned solar and storage—a logical abstraction to allow individuals to independently control their share of the system. We argue that such individual control can benefit all owners and reduce their reliance on grid power. We present mechanisms and algorithms to provide a virtual solar and battery abstraction to users and understand their cost benefits. In doing so, our comparison with a traditional community-owned system shows that our AutoShare approach can achieve the same global savings of 43% while providing independent control of the virtual system. Further, we show that independent energy sharing through virtualization provides an additional 8% increase in savings to individual owners.

A model is developed to optimise power sharing between two grid‐connected prosumers that incorporates photovoltaic and wind energy conversion systems operating in a peer‐to‐peer (P2P) energy‐sharing arrangement. The city of Durban, South Africa, where wind and solar resources are abundant, is selected as a case study to analyse power flows between selected prosumers. The model optimally manages power flows among micro renewables‐based energy generators while minimising the total cost of energy purchased from the power utility under a time‐variable pricing structure. To evaluate the economic feasibility of the suggested peer‐to‐peer energy‐sharing arrangement, a lifecycle cost analysis is conducted to determine when the proposed system will break even, in terms of money spent less achievable salvage value, using the suggested prosumers instead of providing power exclusively from the grid to satisfy load demands. The analysis predicts that the breakeven point will occur after 4.3 years at $13,500, with a projected saving at the end of the 20‐year lifecycle of approximately $60,536.82, or 47.42%. Furthermore, the ‘true’ payback period method is used to assess the economic behaviour of the proposed system with P2P energy‐sharing capabilities and indicates that the total investment cost would be recovered in 8.76 years. These results show that the optimally controlled peer‐to‐peer energy‐sharing scheme is economically feasible in the South African context and may be applied in any global location with similar operating conditions.

With the advent of prosumers, the traditional centralized operation may become impracticable due to computational burden, privacy concerns, and conflicting interests. In this paper, an energy sharing mechanism is proposed to accommodate prosumers' strategic decision-making on their self-production and demand in the presence of capacity constraints. Under this setting, prosumers play a generalized Nash game. We prove main properties of the game: an equilibrium exists and is partially unique; no prosumer is worse off by energy sharing and the price-of-anarchy is 1-O(1/I) where I is the number of prosumers. In particular, the PoA tends to 1 with a growing number of prosumers, meaning that the resulting total cost under the proposed energy sharing approaches social optimum. We prove that the corresponding prosumers' strategies converge to the social optimal solution as well. Finally we propose a bidding process and prove that it converges to the energy sharing equilibrium under mild conditions. Illustrative examples are provided to validate the results., Comment: 15 pages, 8 figures; accepted by IEEE Transactions on Smart Grid

The huge success of electric vehicles across the world is challenged by a lack of infrastructure and a major increase in battery material prices. This challenge positions internal combustion engine vehicles (ICEVs) to remain a vehicle of choice. The majority of these vehicles use a lead-acid battery (LAB) for starting, lighting, and ignition (SLI) functions. However, these LABs are faced with challenges of short lifespan and low storage capacity because of improved electronic systems in modern ICEVs. In this manuscript, we propose an extension application of a hybrid LAB and lithium-ion energy storage system (ESS) for a vehicle using a single source of 70 Ah and 90 Ah capacity. Whereas previously, a hybrid energy storage system (HESS) for use in a vehicle using a source of 50 Ah battery capacity was proposed. Hence, the unique contribution of the study is using an integrated fuzzy-logic and triple-loop-proportional-integral-based battery management strategy (BMS) to improve LAB performance in a wide range of vehicles with different battery capacities sizes. The results show that the proposed BMS can help increase LAB lifespan and improve the storage capacity of the system, thus ensuring reliability. Additionally, compared to a single use of LAB, the combined energy storage system shows superior performance.

The transition towards more sustainable energy management can be supported by the diffusion of energy communities, i.e., coalitions of prosumers that are willing to exchange the energy produced locally. The optimization of energy management requires the solution of a prosumer problem that can become impractical when the number of users increases. This paper presents a parallel approach, based on an edge computing architecture, which is suitable for large communities. The users are partitioned into groups whose proportions, in terms of producers and consumers, mirror the composition of the whole community. The prosumer problems for the different groups are first solved separately and in parallel by local edge nodes. Then, the solutions are combined by a central entity to redistribute the energy among the groups and minimize the exchange of energy with the external grid. A set of experiments show that the parallel approach, when compared with an approach that solves the optimization problem in a single stage, leads to a notable reduction of computing resources, and becomes feasible in large communities for which the single-stage approach is impossible. Moreover, the achieved solution is close to the optimal solution in terms of energy costs.

Radiation (RT) remains the most frequently used treatment against cancer. The main limitation of RT is its lack of specificity for cancer tissues and the limited maximum radiation dose that can be safely delivered without damaging the surrounding healthy tissues. A step forward in the development of better RT is achieved by coupling it with other treatments, such as photodynamic therapy (PDT). PDT is an anti-cancer therapy that relies on the light activation of non-toxic molecules—called photosensitizers—to generate ROS such as singlet oxygen. By conjugating photosensitizers to dense nanoscintillators in hybrid architectures, the PDT could be activated during RT, leading to cell death through an additional pathway with respect to the one activated by RT alone. Therefore, combining RT and PDT can lead to a synergistic enhancement of the overall efficacy of RT. However, the involvement of hybrids in combination with ionizing radiation is not trivial: the comprehension of the relationship among RT, scintillation emission of the nanoscintillator, and therapeutic effects of the locally excited photosensitizers is desirable to optimize the design of the hybrid nanoparticles for improved effects in radio-oncology. Here, we discuss the working principles of the PDT-activated RT methods, pointing out the guidelines for the development of effective coadjutants to be tested in clinics.

Currently, there is few research works focusing on the optimal power dispatch of hybrid renewable energy systems operating in conjunction with hybrid energy storage systems, precisely the combination of pumped hydro storage and battery storage systems. Moreover, there is a lack of studies that focusing on analysing the potential energy cost reduction resulting from the economic power dispatch applied to hybrid energy systems combining grid-interactive renewable energy sources with hybrid energy storages under the peer to peer energy sharing scheme. Given the fact that each of these concepts has the potential benefit of reducing the operation energy cost; this study proposes an optimal energy management model of two grid-interactive prosumers operating in a peer to peer energy sharing mode to supply the loads both from the hybrid renewable sources and hybrid storage systems whilst minimizing the cost of energy purchased from the national grid. Simulation were conducted using different scenarios linked to the internal power sharing pricing structures. The results showed that the proposed arrangement has the potential to reduce substantial energy cost; decrease the reliance of the prosumer from the grid as well as reducing the need of having a larger storage.

Multi-energy complementary microgrids (MECMs) provide an important means to accommodate renewable energy sources due to their abundant adjustable resources and flexible operation modes. However, limited capacity and controllability are the main obstacles that prevent MECMs from participating in the market. In this study, we develop a transactive energy (TE) mechanism-supported energy sharing strategy to coordinate interconnected MECMs in a regional integrated energy system (RIES), where the uncertainty of renewable energy and loads is taken into account via stochastic programming. An RIES operator is introduced to trade with the utility grid as an intermediate player between the electricity market and MECMs. For the TE mechanism, we employ alternating direction method of multipliers (ADMM) algorithm to achieve distributed optimization of energy sharing, which is based on the average of the shared energy residual over all MECMs. A clear economic interpretation exists in the method, wherein shared electrical and thermal energy prices can be obtained. Case studies demonstrate the effectiveness of the multi-energy sharing scheme considering integrated demand response (IDR). Moreover, the distributed algorithm can be implemented and converge easily while respecting MECMs’ individual benefits and private information.

This article presents an efficient peer-to-peer energy-sharing framework for numerous community prosumers to reduce energy costs and to promote renewable energy utilization. Specifically, for day-ahead and real-time energy management of prosumers, an intercommunity energy-sharing strategy and an intracommunity energy-sharing strategy are proposed, respectively. In the former strategy, prosumers can share energy with any community peers, and community aggregators represent their own prosumers to coordinate energy sharing. A two-phase model is designed. In the first phase, the optimal energy-sharing profiles of prosumers are derived to minimize the global energy costs, and in the second phase, equilibrium-based energy-sharing prices are induced considering the individual interests of prosumers. In the latter strategy, prosumers share energy only with its community peers for time saving to handle real-time uncertainties collaboratively to reduce real-time costs. The framework efficiency is verified by the simulation cases on a typical distribution network.

This work examines trajectory learning, clustering, and user association policies for dynamically connectable unmanned aerial vehicle base stations (UAV-BSs). Here, UAV-BSs are allowed to dynamically form physically-connected collocated antenna arrays that enable joint transmission and cooperative energy sharing among the connected UAV-BSs. The UAVs’ trajectories, their clustering decisions, and the user association are jointly determined for the case with static users by maximizing a proportional-fair objective given by the sum log-rate of all users. Then, for the case with dynamic users, the UAVs’ trajectory learning, re-clustering, and user handover policies are developed to adapt to changes in the users’ locations. In particular, the UAVs’ locations are adjusted gradually in each time slot based on a stochastic gradient ascent algorithm, and user handover decisions are made in each time slot based on a reward function that is inspired by the solution in the static case. The clustering decisions are updated every ${T}$ time slots by combining two UAV clusters or by separating a cluster into two. To determine the transmission schemes in each time slot, a semi-orthogonal user scheduling policy is first employed to determine the users that are served in each time slot. Then, joint design of the transmit beamformers and powers is proposed to maximize the sum of log signal-to-leakage-plus-noise ratio (SLNR) of scheduled users. Simulations are provided to demonstrate the effectiveness of the proposed schemes.

Recent legislation enables electricity consumers in multi-apartment buildings to use electricity generated by their neighbors' solar photovoltaic (PV) panels. The association of several residents with PV production (prosumers) is also allowed, increasing the value of the total installed production capacity, also increasing the scale factor of prosumers. This work explores shared self-consumption in a multi-apartment building, and based on real electricity production and consumption data, an optimization model based on Mixed Integer Linear programming (MILP) was implemented. The main goal was to minimize the electricity consumption costs from the external electrical network of each apartment, maximizing the benefits of non-production consumers in joining the self-consumption program, and maximizing the profits of prosumers. A sensitive analysis of total cost and prosumer/consumer benefit was evaluated. The obtained results show that prosumers and consumers can both have interesting financial benefits for participating in shared PV generation programs.

The concept of a virtual energy storage system (VESS) is based on the sharing of a large energy storage system by multiple units; however, the capacity allocation for each unit limits the operation performance of the VESS. This study proposes an operation strategy of a dynamic VESS for smart energy communities. The proposed VESS operation strategy considers the usage-limited constraint rather than the capacity allocation constraint and it guarantees the usage of VESS resources of each participant for an operation period. Therefore, the degrees of freedom for VESS operation can be increased at each operation time. The dynamic VESS operation problem is formulated as a mixed-integer linear problem that could be solved optimally by applying gradient methods and dual decomposition. The dataset of a VESS in Korea is used for simulation. The simulation results demonstrate that, when the proposed operation strategy is used, the cost efficiency achieved is more than twice that achieved when the existing VESS operation strategy is used. Furthermore, the proposed strategy accurately reflects the characteristics of the participants; thus, more units can participate in the VESS operation service. The proposed VESS operation can improve the system performance of the utility grid and increase the net benefit of the participants.

A distributed congestion management scheme based on iterative distribution locational marginal price (iDLMP) to integrate distributed energy resources into the distribution network has been studied recently. However, this scheme has a requirement on the optimisation formulation for prosumers, i.e. the suboptimisation problem should be a convex one, which in certain cases might face challenges. A typical example is the non-linear constraints of the energy storage system (ESS), which ensures that charging and discharging of ESS are mutually exclusive. In this study, the authors consider the prosumer as a self-organising unit to integrate diverse resource flexibilities. Two roles exist in prosumer's energy operation: as a producer and as a consumer, which are also mutually exclusive for one prosumer at one time. When applying the iDLMP method to integrate prosumers energy operation, one meets a similar challenge as before. Thus, they make some reasonable assumptions about the transformer capacity, line capacity, and the price sensitivity parameters to convexify the optimisation problem. With these assumptions, a distributed convex optimisation framework is developed for the prosumers that can handle the non-convexity issue in the optimisation problem. The numerical results indicate that the proposed method can achieve local energy sharing in the distribution network, respecting the line congestion alleviation.

In this paper, we consider a cellular system, in which base stations (BSs) are powered by both on-grid and renewable energy sources. To efficiently utilize the harvested energy of the BSs, we study adaptive traffic management (TM) and energy cooperation (EC) that aim at minimizing the on-grid energy consumption, while guaranteeing minimum average throughputs. To achieve this, we develop an adaptive TM and EC algorithm that jointly decides the energy sharing among BSs, the user association to BSs, and the sub-channel and power allocation in BSs. Within the algorithm, a network scheduling problem, which is mixed-integer non-linear programming (MINLP), should be solved in each timeslot. To efficiently solve it, we develop a network scheduling algorithm applying generalized Benders decomposition (GBD) that optimally solves the MINLP problem. In addition, we also develop a heuristic network scheduling algorithm that has a much lower computational complexity than the GBD algorithm, while providing comparable performance. Through the numerical results, we show that our algorithms always outperform the algorithms that use only one of TM or EC regardless of the system conditions.

A transactive energy coordination mechanism is proposed in this study where community microgrids are supplying power to multi-dwelling residential apartments. The proposed transactive energy coordination mechanism coordinates the energy sharing among apartments based on the energy profile of the community microgrid where the excess energy is traded with non-contributing apartments. The proposed coordination mechanism is embedded within an energy management controller which uses the energy profile and determines the valuation of energy. A choice factor along with the bound on electricity prices is also incorporated to calculate the bidding price and a double-sided auction mechanism is considered for the bidding purpose. The utility maximisation approach is used to clear the market. Different scenarios based on the flexibility in the pricing strategy are considered to evaluate the performance of the proposed transactive energy coordination mechanism. The potential economic benefits of the proposed scheme are also analysed which clearly demonstrate that it is beneficial for all participants.

With the rapid growth in clean distributed energy resources involving micro-generation and flexible loads, users can actively manage their own energy and have the capability to enter in a market of energy services as prosumers while reducing their carbon footprint. The coordination between these distributed energy resources is essential in order to ensure fair trading and equality in resource sharing among a community of prosumers. Peer-to-Peer (P2P) networks can provide the underlying mechanisms for supporting such coordination and offer incentives to prosumers to participate in the energy market. In particular, the federation of energy clusters with P2P networks has the potential to unlock access to energy resources and lead to the development of new energy services in a fast-growing sharing energy economy. In this paper, we present the formation and federation of smart energy clusters using P2P networks with a view to decentralise energy markets and enable access and use of clean energy resources. We implement a P2P framework to support the federation of energy clusters and study the interaction of consumers and producers in a market of energy resources and services. We demonstrate how energy exchanges and energy costs in a federation are influenced by the energy demand, the size of energy clusters and energy types. We conduct our modelling and analysis based on a real fish industry case study in Milford Haven, South Wales, as part of the EU H2020 INTERREG piSCES project.

Heterogeneous network, which is a key technology of fifth generation(5G) mobile communication system, can effectively solve the spectrum resource shortage in the communication system. However, with densified deployment of base stations (BSs) and growth of user quantity and communication data size, the system energy consumption of BSs has imposed enormous economic pressure on network operators. As a result, energy consumption decreases and cost of communication system becomes a problem that requires urgent solutions. Renewable energy power generation devices of BSs have provided an opportunity for solving this problem with the development of smart power grids. However, the production rate of renewable energy sources presents intense volatility because of the influence of weather factors. This condition brings new challenges to the energy allocation of communication system. Therefore, an energy sharing link between BSs was established in a heterogeneous wireless network, and the distance between macro-BS and micro-BS was taken as an index. The energy consumption-based energy and user joint allocation method and energy cost-based energy and user joint allocation method were proposed. The two multi-objective optimization problems were converted into two convex optimization problems, and the optimal allocation strategies were solved using convex optimization toolbox. Simulation results indicate that the energy consumption-based allocation method takes energy consumption as the optimization objective and makes the shared link transmit energy sources as few as possible. This way reduces energy consumptions of the link and the system. Starting from the economic angle, the energy cost-based allocation method considers the influence of energy price, coordinates energy allocation between BSs based on energy cost using the shared link, and optimizes energy allocation among BSs at each time slot. Therefore, the method can reduce the energy cost of the system by a large margin.

Energy is one of the key enablers for robotic swarms. The feasibility of heterogeneous swarms to perform complex missions depends upon the energy stored in the robot batteries. Allowing multiple access energy transfer (MAET) in robot swarms provides more flexibility to accomodate uncertain missions. In this paper, we present a MAET solution for heterogeneous swarms using code division multiple access wireless power transfer (CDMA-WPT). CDMA-WPT allows robots in heterogeneous swarms to exchange energy while in motion, allows faster charging rates by enabling multiple robots to exchange energy, is easily scalable, offers power flow selectivity to enable energy encryption, and does not require a centralized controller. The encoding scheme, power flow model, and code construction algorithm for a particular implementation of CDMA-WPT is presented. The proof of concept is verified using a hardware implementation for a heterogeneous swarm of four robots. Finally, the application of CDMA-WPT to a heterogeneous swarm with 30 robots is presented.

Citizens are set to play an active role in the energy transition by transforming from ‘passive’ consumers to ‘active’ prosumers. Renewable Energy Communities (RECs) are envisioned as modes of collective prosumership by citizens under the Renewable Energy Directive of 2018 (RED II). A holistic understanding of RECs is essential to identify the benefits and challenges of collective prosumership. RECs have been the topic of several modelling studies, but a single model that simulates RECs from an integrated perspective - combining technical, economic and ecological analysis - is absent. Wide variability in the indicators discourages comparison of the results across studies. This article builds on the existing knowledge by proposing an integrated model to undertake a multi-disciplinary assessment of a potential REC. First, the proposed model analyses the technical possibilities of collective prosumership using energy flow analysis based on consumption and generation profiles. Second, the model evaluates the economic impacts of prosumership from two perspectives: from the consumers’ perspective (in terms of the annual cost of energy consumption) and from an investor’s perspective (in terms of the net present value of the investment). Thirdly, the model quantifies the annual greenhouse gas emissions of energy consumption (expressed in CO2 equivalent) to evaluate the ecological impact of prosumership. Lastly, a set of key performance indicators (KPIs) are proposed that can be used to interpret and compare the results of simulations and are mapped to the actors in the REC in line with their objectives. The proposed approach offers a single, replicable model that can be used to simulate RECs in the different Member States of the European Union. The KPIs can be used to compare the impact of combinations of various prosumership activities within the same REC or to compare two different RECs on the benefits offered vis-a-vis the investments incurred. The KPIs also offer insights into the aligning and conflicting objectives of the stakeholders of the REC.

24 figures, 7 tables.-- © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/, Energy harvesting from renewable sources can play a vital role to decarbonize the environment, limit global warming and mitigate the growing energy demand. The objective of this work consists of decarbonizing a University Campus and neighboring communities by producing electricity from solar photovoltaic systems integrated with an energy storage system and local grid station. A new mathematical model is developed to maximize the system power generation and balance the load demand. The simulation and optimization software System Advisor Model (SAM) is used to develop and test model results. The software is used to analyze and optimize the solar energy generation, the energy demand, and the economic performance: capital cost, overall investment, net present value, and the Levelized Cost of Energy of the project. A novel approach decentralized load centers is adopted to share power with adjacent communities. At the aim of improving the system flexibility, reliability, and climate resilience, the established model is grid-connected. The CO2 emissions reduction is also determined to evaluate the environmental impact of the interventions.

Blockchain has become the most revolutionary technology in the 21st century. In recent years, one of the concerns of world energy isn't just sustainability yet, in addition, being secure and reliable also. Since information and energy security are the main concern for the present and future services, this thesis is focused on the challenge of how to trade energy securely on the background of using distributed marketplaces that can be applied. The core technology used in this thesis is distributed ledger, specifically blockchain. Since this technology has recently gained much attention because of its functionalities such as transparency, immutability, irreversibility, security, etc, we tried to convey a solution for the implementation of a secure peer-to-peer (P2P) energy trading network over a suitable blockchain platform. Furthermore, blockchain enables traceability of the origin of data which is called data provenience. In this work, we applied a secure blockchain technology in peer-to-peer energy sharing or trading system where the prosumer and consumer can trade their energies through a secure channel or network. Furthermore, the service management functionalities such as security, reliability, flexibility, and scalability are achieved through the implementation. \\ This thesis is focused on the current proposals for p2p energy trading using blockchain and how to select a suitable blockchain technique to implement such a p2p energy trading network. In addition, we provide an implementation of such a secure network under blockchain and proper management functions. The choices of the system models, blockchain technology, and the consensus algorithm are based on literature review, and it carried to an experimental implementation where the feasibility of that system model has been validated through the output results.

Energy communities are gaining increasing relevance in the transition toward more decentralized, decarbonized and digitalized energy systems. They represent a heterogeneous phenomenon, a characteristic that explains the difficulty in providing a definition of what they are. This chapter offers some conceptual clarity by building a comprehensive taxonomy of energy communities that practitioners can rely on. Five different categories of communities are distinguished based on their main function in the energy system and the level of maturity of their business models. This chapter also highlights the strengths and weaknesses of the different categories and the policy and regulatory issues they raise.

Peer-to-peer (P2P) renewable power sharing within a building community is a promising solution to enhance the community's self-sufficiency and relieve the grid stress posed by the increased deployment of distributed renewable power. Existing studies have pointed out that the energy sharing potentials of a building community are affected by various factors including location, community scale, renewable energy system (RES) capacity, energy system type, storage integration, etc. However, the impacts of these factors on the energy sharing potentials in a building community are not fully studied. Being unaware of those factors’ impacts could lead to reduced energy sharing potentials and thus limit the associated improvement in energy and economic performances. Thus, this study conducts a comprehensive analysis of various factors’ impacts on the energy sharing performances in building communities. Two performance indicators are first proposed to quantify the energy sharing performances: total amount of energy sharing and energy sharing ratio (ESR). Then, parametric studies are conducted based on real electricity demand data in three countries to reveal how these factors affect the proposed indictors and improvements in self-sufficiency, electricity costs, and energy exchanges with the power grid. Next, a genetic algorithm based design method is developed to optimize the influential parameters to maximize the energy sharing potentials in a community. The study results show that the main influential factors are RES capacity ratio, PV capacity ratio, and energy storage system capacity. A large energy storage capacity can enhance the ESR. To achieve the maximized ESR, the optimal RES capacity ratio should be around 0.4 ∼ 1.1. The maximum energy sharing ratio is usually smaller in high latitude districts such as Sweden. This study characterizes the energy sharing performances and provides a novel perspective to optimize the design of energy systems in energy sharing communities. It can pave the way for the large integration of distributed renewable power in the future. © 2022 The Author(s)

Distributed energy resources (DERs) are being embedded rapidly and widely in the power grid and promoting the transformation of the centralized power industry to a more deregulated mode. However, how to safely and efficiently consume renewable energy is becoming a major concern. In this regard, energy sharing at both grid-scale and community-scale has emerged as a new solution to encourage participants to actively bid instead of acting as price takers and has the potential to accelerate the integration of DERs and decrease energy costs. At the grid level, two risk-averse energy sharing models are developed to safely integrate renewable energy by considering the network constraints and overbidding risk. A risk-averse two-stage stochastic game model is proposed for the regional energy sharing market (ESM). The sample average approximation (SAA) method is used to approximate the stochastic Cournot-Nash equilibrium. In addition, a data-driven joint chance-constrained game is developed for energy sharing in the local energy market (LEM). This model considers the maximum outputs of renewable energy aggregators (REAs) are random variables whose probability distributions are unknown, but the decision-maker has access to finite samples. Case studies show that the proposed game models can effectively increase the profit of reliable players and decrease the overbidding risk. At the community level, a community server enables energy sharing among users based on the Bayesian game-based pricing mechanism. It can also control the community energy storage system (CESS) to smooth the load based on the grid's price signal. A communication-censored ADMM for sharing problems is developed to decrease the communication cost between the community and the grid. Moreover, a co-optimization model for the plan and operation of the shared CESS is developed. By introducing the price uncertainty and degradation cost, the proposed model could more accurately evaluate the performance of the CESS and tap more economic potential. This thesis provides proof of the Nash equilibrium of all game models and the convergence of all market clearing algorithms. The proposed models and methods present performance improvement compared with existing solutions. The work in this thesis indicates that energy sharing is possible to implement at different levels of the power system and could benefit the participants and promote the integration of DERs.

The rapid growth of microgrid with various dis-tributed energy resources (DERs) brings new opportunities for local energy sharing in the microgrid. However, the uncertainties of renewable distributed generation and loads pose a great tech-nical challenge for a microgrid operator (MGO). Thus, this paper proposes a transactive energy sharing (TES) approach for the MGO and DER aggregators to minimize the total social cost, considering network operating constraints. Accordingly, a two-settlement transactive energy (TE) market with an incentive en-ergy pricing scheme is developed to encourage the participation in the energy sharing. Besides, the real-time energy transactions of the aggregators are considered in a day-ahead optimization stage to address their negative impacts on microgrid operation. To solve the proposed TES problem, an alternating direction method of multipliers (ADMM) is applied. The uncertainties in each ADMM local problem are further addressed by an adaptive robust optimization (ARO) method solved by a column-and-constraint generation (C&CG) algorithm. The updated dual and coupling variables at each ADMM iteration could interact with the C&CG algorithm, impairing ADMM convergence. To solve this issue, an alternating uncertainty-update procedure is devel-oped. The simulation results verify the high efficiency and solu-tion robustness of the proposed TES method.

With the renewable energy directive of the European Commission the path has been set toward more emancipated end consumers in the energy system, by allowing shared energy use schemes such as renewable energy communities. These were transposed into national law in Austria in July 2021. But even before that, energy could be shared between a producer/prosumer and one or multiple users via a so-called direct line or direct line system. While the technical and legal requirements for operating a direct line (system) are a major barrier to overcome, direct line (systems) have for a long time been the only means to exchange renewable energy between different buildings in the Austrian energy system. In the municipality of Thannhausen such a direct line system with multiple end users has been installed and is in a test phase since September 2021. During the implementation of the direct line system the legal framework for energy sharing has changed and renewable energy communities have been introduced in Austria. The question arose, whether the direct line system can compete with renewable energy communities. In this paper a comparison between these two types of energy sharing schemes, a direct line system and a renewable energy community is made on basis of the Thannhausen pilot. As mentioned before, in Thannhausen, a direct line system, consisting of multiple direct lines to connect eight consumers with one photovoltaic generator was built. For the same configuration a renewable energy community was investigated by means of a simulation model. Even if it might seem, that the direct line system is outdated by the new possibilities which are provided by the renewable energy communities, a closer look showed, that both systems have their advantages and disadvantages. For the direct line system structural measures are necessary, which result in a certain amount of investment costs which are higher than for a renewable energy community, where the public grid is used. In contrast to that, no grid fees have to be paid for the energy distributed via the direct line system. Another advantage of the direct line system is the possibility of an islanding-mode in case of a disruption of the public grid. However, it always depends very much on the initial situation. Direct line systems are an attractive solution only in cases where the consumers are located closely to each other, so that the digging effort is manageable. In case of the Thannhausen pilot, the evaluation showed, that the direct line system enables similar savings for the participants to a renewable energy community, when the subsidies granted are taken into account. Since there are no grid fees for the direct line system, a higher feed-in tariff is made possible for the municipality, which amortises the higher investment costs, so that the direct line system has even the higher net present value at the end of the period under consideration., Mobility, Knowledge and Innovation Hubs in Urban and Regional Development. Proceedings of REAL CORP 2022, 27th International Conference on Urban Development and Regional Planning in the Information Society, 175-184

The use of energy sharing models in smart grids has been widely addressed in the literature. However, feasible technical solutions that can deploy these models into reality, as well as the correct use of energy forecasts are not properly addressed. This paper proposes a simple, yet viable and feasible, solution to deploy energy management systems on the end-user-side in order to enable not only energy forecasting but also a distributed discriminatory-price auction peer-to-peer energy transaction market. This work also analyses the impact of four energy forecasting models on energy transactions: a mathematical model, a support-vector machine model, an eXtreme Gradient Boosting model, and a TabNet model. To test the proposed solution and models, the system was deployed in five small offices and three residential households, achieving a maximum of energy costs reduction of 10.89% within the community, ranging from 0.24% to 57.43% for each individual agent. The results demonstrated the potential of peer-to-peer energy transactions to promote energy cost reductions and enable the validation of auction-based energy transactions and the use of energy forecasting models in today’s buildings and end-users., This article is a result of the project RETINA (NORTE-01-0145-FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Portuguese Foundation for Science and Technology (FCT) under grants 2021.07754.BD and CEECIND/01179/2017.

With the development of distributed generation and demand-side response, traditional consumers are now converted into prosumers that can actively produce and consume electricity. Moreover, with the help of energy integration technique, prosumers are encouraged to form a multi-energy community (MEC), which can increase their social welfare through inside multi-energy sharing. This paper proposes a day-ahead cooperative trading mechanism in a MEC that depends on an energy hub (EH) to couple electricity, natural gas, and heat for all prosumers. The model of the traditional uncooperative local integrated energy system (ULIES) is also built as a comparison. A satisfaction-based profit distribution mechanism is set according to prosumers’ feelings about the extra cost they save or extra profit they gain in MEC compared with that in ULIES. Finally, case studies are set to analyze the utility of MEC in enlarging social welfare, after considering the effects of prosumers’ electricity usage patterns and buy-and-sell prices in retail market. The results of satisfaction-based profit distribution are also analyzed to verify that it can save the cost or increase the profit of each prosumer and EH.

It is of great significance to introduce the conception of a sharing economy into the electricity industry, which can promote the dispatch of multiple integrated energy systems. On the one hand, it is difficult to reveal the behaviors of complex players with multi-energy coupling through the traditional centralized optimization method of single electric energy. On the other hand, the uncertain fluctuations of renewable energy, such as wind power and photovoltaic, have posed great challenges to market transactions. First, the relationship and the functions of all stakeholders in the system are described in this paper, followed by the establishment of flexible resource models such as demand response and energy storage devices. On this basis, a low-carbon dispatching framework of multiple regional gas–electric integrated energy systems is then constructed under the guidance of cooperative game theory. The contribution indexes are established to measure the degree of energy sharing among the subsystems, and the method of asymmetric Nash bargaining is used to settle the interests of each subsystem. Second, a robust optimization model of multiple regional systems is established in response to multiple uncertainties from renewable energy and load. Finally, the numerical example proves that the proposed mechanism can increase the benefits of each integrated energy system player. Moreover, it helps the system to yield optimal benefits in the face of uncertainties and provides a reference on how to realize energy sharing under uncertainties from source load.

The aim of this paper is to assess opportunities the Clean Energy Package provides for Plus Energy Buildings (PEBs) and Plus Energy Districts (PEDs) regarding their economic optimization and market integration, possibly leading to new use cases and revenue streams. At the same time, insights into regulatory limitations at the national level in transposing the set of EU Clean Energy Package provisions are shown. The paper illustrates that the concepts of PEBs and PEDs are in principle compatible with the EU energy community concepts, as they relate to technical characteristics while energy communities provide a legal and regulatory framework for the organization and governance of a community, at the same time providing new regulatory space for specific activities and market integration. To realize new use cases, innovative ICT approaches are needed for a range of actors actively involved in creating and operating energy communities as presented in the paper. The paper discusses a range of different options to realize PEBs and PEDs as energy communities based on the H2020 EXCESS project. It concludes, however, that currently the transposition of the Clean Energy Package by the EU Member States is incomplete and limiting and as a consequence, in the short term, the full potential of PEBs and PEDs cannot be exploited.

With the rapid proliferation of wireless traffic and the surge of various data-intensive applications, the energy consumption of wireless networks has tremendously increased in the last decade, which not only leads to more CO2 emission, but also results in higher operating expenditure. Consequently, energy efficiency (EE) has been regarded as an essential design criterion for future wireless networks. This paper investigates the problem of EE maximisation for a cooperative heterogeneous network (HetNet) powered by hybrid energy sources via joint base station (BS) switching (BS-Sw) and power allocation using combinatorial optimisation. The cooperation among the BSs is achieved through a coordinated multi-point (CoMP) technique. Next, to overcome the complexity of combinatorial optimisation, Lagrange dual decomposition is applied to solve the power allocation problem and a sub-optimal distance-based BS-Sw scheme is proposed. The main advantage of the distance-based BS-Sw is that the algorithm is tuning-free as it exploits two dynamic thresholds, which can automatically adapt to various user distributions and network deployment scenarios. The optimal binomial and random BS-Sw schemes are also studied to serve as benchmarks. Further, to solve the non-fractional programming component of the EE maximisation problem, a low-complexity and fast converging Dinkelbach’s method is proposed. Extensive simulations under various scenarios reveal that in terms of EE, the proposed joint distance-based BS-Sw and power allocation technique applied to the cooperative and harvesting BSs performs around 15–20% better than the non-cooperative and non-harvesting BSs and can achieve near-optimal performance compared to the optimal binomial method.

A virtual energy storage system (VESS) logically shares a physical energy storage system among multiple units. In resource sharing, the distribution of benefits is a critical problem. As a resolution, this study proposes a fair VESS operation method for smart energy communities that involve groups of energy consumption units. First, the cost and resource fairness indices are defined as the benefit and VESS usage proportional to the investment cost, respectively. The fair VESS operation problem is formulated considering the fairness indices that could be solved optimally by applying gradient methods without additional computational burden. The simulation results using the dataset in Korea demonstrate that the proposed operation allows the fair distribution of the benefit and resource usage among units with a marginal benefit reduction of approximately 5% in relation to the VESS operation to maximize the benefit. Moreover, it is shown that the resource fairness that controls the VESS usage limits the total benefit, and the cost fairness distributes the benefit among units according to the cost contribution. Furthermore, the proposed VESS operation can manage the VESS lifetime and improve the system performance of the utility grid.

Consumer investment in distributed energy resources (DERs) is increasing the penetration of renewable energy in the grid. In some cases, DERs produce more electricity than needed by the owner and this excess electricity is sold to the utility (e.g., net metering). In contrast, energy sharing allows a facilitator, which may or may not be the utility, to redistribute excess renewable electricity to fellow community members directly. However, little is known about consumer interest in participating in this type of arrangement. This preregistered study uses structural equation modeling to compare two behavioral theories, Value-Belief-Norm and Diffusion of Innovation, to predict consumer interest in participating in energy sharing. Participants answered questions about energy sharing in the context of an energy-sharing community facilitated by the fictional company, E-topia. Survey data from 195 online participants suggest that Value-Belief-Norm is a better, although not quite acceptable, fit. This suggests that early adoption of energy sharing may be driven by appealing to core values rather than novelty-seeking. This study implies that individuals are more likely to participate in a new technology system such as energy sharing when the effects of participation align with individuals’ values.

Funding Information: The first author is supported by a personal grant from The Finnish Foundation for Technology Promotion/The Foundations' Post Doc Pool. This paper was partly funded by two Academy of Finland projects: “Advanced Energy Matching for Zero-Energy Buildings in Future Smart Hybrid Networks 2014-2018, Decision no. 277680” and the Strategic Research Council (SRC) project “Smart Energy Transition (SET) – Realizing Its Potential for Sustainable Growth for Finland’s Second Century, Decision no. 314325”. The work is connected to the authors’ participation in the IEA-EBC Annex 67 – Energy Flexible Buildings (http://www.annex67.org/). Publisher Copyright: © 2021 The Authors Copyright: Copyright 2021 Elsevier B.V., All rights reserved. Exporting generated electricity by on-site renewable energy systems from buildings to the grid is only slightly profitable in many countries. Therefore, it is required to investigate the benefits of sharing generated energy in a microgrid within a community of buildings. Exploiting the benefits of peer-to-peer energy exchange between prosumers in a community can make the best use of the on-site generation while reducing their bills. This study elaborates the potential of energy management to minimize the electricity cost of a community consisted of multiple buildings and connected to a microgrid. To implement this, an energy management system is designed based on non-linear economic model predictive control and successive linear programming for sharing the on-site surplus generated electricity between the buildings in the community. Four buildings are simulated and studied as an example of a small community. These buildings are dissimilar in their age, thermal mass, insulation, heating system and on-site renewable energy systems. It is shown that considering the community of buildings as a single entity, the novel model predictive control can be efficiently used for minimizing the energy cost of the community that has various sources of energy generation, conversion and storage, including significant non-linear interactions. Three different scenarios of the energy management system for the studied community are investigated, and the results indicate that the annual electricity energy cost for single buildings can be reduced by 3.0% to 87.9%, depending on the building and its systems, and by 5.4% to 7.7% on the community level.

The conventional electric power system is undergoing a transition from high fossil fuel dependence to significant renewable energy share primarily due to decreasing costs of renewable technologies, increasing environmental pollution, and favorable energy policies. The introduction of distributed energy resources with minimal power losses has also recently promoted the DC power systems deployment at small scale such as community-based DC microgrids. These systems allow the possibility of trading surplus energy from distributed energy resources with peer-to-peer (P2P) energy sharing. As power losses in a sharing scheme are non-linear concerning the distance between trading prosumers and power trade level, therefore, P2P energy sharing cannot be optimally managed with conventional factory-warehouse transportation techniques. In this work, we modeled a DC microgrid system with P2P sharing using a non-linear programming technique which allows the users to share their surplus energy from distributed energy resources with minimal system losses including distribution losses as well as conversion losses in comparison to conventionally employed factory-warehouse transportation technique. The proposed model is applied to a community microgrid having independent photovoltaic (PV) and battery systems installed at each house. Results show that the total system losses are reduced by up to 25% with the proposed optimization framework as compared to conventional factory-warehouse transportation sharing mechanism.

Motivated by the ever-increasing installation of photovoltaics in homes and small businesses, we consider a general small-scale market for agent-to-agent (energy) resource sharing, in which each agent could either be a producer (server) or a consumer (client) in each time period. We use a simple mechanism under which servers and clients are matched at random, and may each choose an offered price. If the client offers more than the server proposes, the transaction is successful. We model this system in the form of a mean field game, and prove the existence of a mean field equilibrium in which the server's bid is a fixed price that the client either accepts or rejects based on its current budgetary surplus. We also show in simulations that the system can attain near 100% trade ratios. Finally, using a case study over a synthetic electric grid, we show that the system can benefit both individuals and the system as a whole.