Reliability of Dynamic Load Scheduling with Solar Forecast Scenarios

This paper presents and evaluates the performance of an optimal scheduling algorithm that selects the on/off combinations and timing of a finite set of dynamic electric loads on the basis of short term predictions of the power delivery from a photovoltaic source. In the algorithm for optimal scheduling, each load is modeled with a dynamic power profile that may be different for on and off switching. Optimal scheduling is achieved by the evaluation of a user-specified criterion function with possible power constraints. The scheduling algorithm exploits the use of a moving finite time horizon and the resulting finite number of scheduling combinations to achieve real-time computation of the optimal timing and switching of loads. The moving time horizon in the proposed optimal scheduling algorithm provides an opportunity to use short term (time moving) predictions of solar power based on advection of clouds detected in sky images. Advection, persistence, and perfect forecast scenarios are used as input to the load scheduling algorithm to elucidate the effect of forecast errors on mis-scheduling. The advection forecast creates less events where the load demand is greater than the available solar energy, as compared to persistence. Increasing the decision horizon leads to increasing error and decreased efficiency of the system, measured as the amount of power consumed by the aggregate loads normalized by total solar power. For a standalone system with a real forecast, energy reserves are necessary to provide the excess energy required by mis-scheduled loads. A method for battery sizing is proposed for future work.Comment: 6 pager, 4 figures, Syscon 201

Modelling of an Intelligent Microgrid System in a Smart Grid Network

To achieve the goal of decarbonising the electric grid by 2050 and empowering energy citizen, this research focuses on the development of Microgrid (μGrid) systems in Irish environment. As part of the research work, an energy efficient and cost effective solution for μGrid, termed Community-μGrid (C-μGrid) is proposed. Here the users can modify their micro-Generation (μGen) converters to facilitate a single inverter in a C-μGrid structure. The new system could allow: (i) technological advantage of improved Power Quality (PQ); (ii) economic advantage of reduced cost of energy (COE) to achieve sustainability. Analysis of scenarios of C-μGrid (AC) systems is performed for a virtual community in Dublin, Ireland. It consists of (10 to 50) similar type of residential houses and assumes that each house has a wind-based μGen system. It is found that, compared to individual off-grid μGen systems, an off-grid C-μGrid can reduce upto 35% of energy storage capacity. Thus it helps to reduce the COE from €0.22/kWh to 0.16/kWh. In grid connected mode, it can sell excess energy to the grid and thus COE further decreases to €0.11/kWh. Thus a cost-effective C-μGrid is achieved. The proposed system can advance its energy management efficiency through implementation of Demand Side Management (DSM) technique. For the test case, 50% of energy storage capacity could be avoided through DSM technique. It also helps to further decrease the COE by 25%. The C-μGrid system with storage is optimised by implementing the Economic Model Predictive Control (EMPC) approach operating at the pricing level. Emphasis is given to the operational constraints related to the battery lifetime, so that the maintenance and replacement cost would be reduced. This technique could help to improve the battery performance with optimised storage and also reduces the COE of the system by 25%

Heat pump and thermal storage sizing with time-of-use electricity pricing

Heat pump and thermal storage sizing studies require modelling to ensure capital and operational costs are minimised. Modelling should consider added flexibility, eg grid services, sector coupling benefits, eg utilising excess wind production, and access to electricity markets, eg time-of-use tariffs. This paper presents a two-step methodology for sizing heat pump and thermal storage systems with a time-of-use electricity tariff. The first step is a modelling method for decentralised energy systems, with the broader aim of assisting planning-level design, and consists of resource assessment, demand assessment, electrical components, thermal components, storage components, and control strategies. The second step is a parametric analysis of heat pump and thermal storage size combinations. This is then applied to a sizing study for an existing residential district heating network including a time-of-use electricity tariff. The performance metrics:% of heat pump thermal output at low-cost period,% of heat demand met by heat pump, electricity import cost, and capital cost, were plotted and tabulated to compare sizing combinations. Graphs explore the operation of the heat production units and the thermal storage. Future development involving use of model predictive control and grid services, and alternative applications including operational planning and feasibility studies, are then discussed

Distributed Online Modified Greedy Algorithm for Networked Storage Operation under Uncertainty

The integration of intermittent and stochastic renewable energy resources requires increased flexibility in the operation of the electric grid. Storage, broadly speaking, provides the flexibility of shifting energy over time; network, on the other hand, provides the flexibility of shifting energy over geographical locations. The optimal control of storage networks in stochastic environments is an important open problem. The key challenge is that, even in small networks, the corresponding constrained stochastic control problems on continuous spaces suffer from curses of dimensionality, and are intractable in general settings. For large networks, no efficient algorithm is known to give optimal or provably near-optimal performance for this problem. This paper provides an efficient algorithm to solve this problem with performance guarantees. We study the operation of storage networks, i.e., a storage system interconnected via a power network. An online algorithm, termed Online Modified Greedy algorithm, is developed for the corresponding constrained stochastic control problem. A sub-optimality bound for the algorithm is derived, and a semidefinite program is constructed to minimize the bound. In many cases, the bound approaches zero so that the algorithm is near-optimal. A task-based distributed implementation of the online algorithm relying only on local information and neighbor communication is then developed based on the alternating direction method of multipliers. Numerical examples verify the established theoretical performance bounds, and demonstrate the scalability of the algorithm.Comment: arXiv admin note: text overlap with arXiv:1405.778

Industrial Multi-Energy and Production Management Scheme in Cyber-Physical Environments: A Case Study in a Battery Manufacturing Plant

Among the various electricity consumer sectors, the consumption level of the industrial sector is often considered as the largest portion of electricity consumption, highlighting the urgent need to implement demand response (DR) energy management. However, implementation of DR for the industrial sector requires a more sophisticated and different scheme compared to the residential and commercial sector. This study explores all the elastic segments of plant multi-energy production, conversion, and consumption. We then construct a real-time industrial facilities management problem as an optimal dispatch model to enclose these elastic segments and production constraints in cyber-physical environments. Moreover, a model predictive-based centralised dispatch scheme is proposed to address the uncertainties of real-time price and renewable energy forecasting while considering the sequence of the production process. Numerical results demonstrate that the proposed scheme can enhance energy efficiency and economics of lithium battery manufacturing plant through responding to the real-time price whilst ensuring the completion of production tasks

Model predictive approach for the demand side management of S-market Tuira

Abstract. The decrease of the ecological footprint is crucial for the continuation of modern lifestyle as it is in the future. Decarbonization of the power grid is a major step towards this goal. The renewable resources, such as solar and wind, are becoming increasingly important methods of carbon free energy production. However, their integration to the power grid faces limitations due to their inherent seasonal- and circadian rhythms. Thus, the investment into carbon free energy production is only the first part of the solution, because the intermittent nature results in curtailment phenomena, where increased renewable power generation capacity does not result in increase in renewable penetration in the power grid. To get a completely decarbonized power grid, further technical solutions are needed that enable the renewable penetration into the power grid to rise to 100 %. The power grid has been using top-down architecture, where the aggregator side decides the amount of power generation done by the power plants since its introduction in the beginning of 20th century. In this model, the renewable energy is a disturbance, where power generation side must accommodate to the energy demand drop that the renewable power generation causes. To fix the problem, we also must redesign the power grid infrastructure in a way that high portion of renewable energy does not endanger the power grid stability and controllability. An easier way to manage renewables is by building smart micro-grids, which is an individual power grid with two-way power transfer capabilities. This results to easier decision-making on the grid aggregator side. All the micro-grids that are connected to the main power grid can be instructed how to accommodate their energy production and management to benefit the grid stability of main power grid, and to further improve the estimation of power generation demand of conventional power plants. Furthermore, we must develop tools that the microgrids can utilize to balance power demand across all power grid conditions dictated by the intermittent nature of power demand and renewable energy generation. Beyond the micro-grid introduction, the renewable energy proliferation faces limitations because of the intermittent nature of the renewable energy generation. For example, increasing the capacity of solar power generation will not work to replace conventional power generation after a point if we can only utilize the generated power during daytime. The solution here is to shift the consumption from daytime to night-time. To do this, we need to consider energy storage solutions. This thesis explores the possibility of utilizing cooling as part of power grid management using advanced process control methods in conjunction with an Internet-of-Things approach. More specifically, I investigate the idea of using refrigeration systems as one-way energy sinks that can be used to time-shift the consumption of electricity by storing at peak renewable power generation periods. The target system of the work is the steam-compression-cooling cycle of S-Market Tuira in Oulu, which has a carbon dioxide circulation. In his 2016 Master’s thesis dissertation, Jarno Johannes Tenhomaa has introduced a dynamic linear time variant state model that reflects the dynamics of the cycle, thus telling about the thermal inertia of the cooled products and thus the electricity demand of the cycle. In this work, a Model Predictive Controller is derived using a dynamic model of refrigeration systems, whose function is to control the air temperatures of the store’s refrigerators and freezers, which in turn are followed by the temperatures of the refrigerated products. The MPC control calculates the optimal control trajectory for the cooling system actuator, with which the temperature of the refrigeration system deviates from the set-point temperature as little as possible during the selected prediction horizon. Additionally, the different set-point temperature selection strategies are benchmarked in comparison to each other. The spot-price -based control changes the temperature set-point temperatures of the coolers and freezers according to Nord pool spot price data. The renewable portion data -based control strategies are based on power production data for the power grid in Finland provided by organization Energiateollisuus. For the purposes of testing the MPC controller and control strategy benchmarking, I have built a real-time interactive simulator in Matlab -program to illustrate the function and dynamics of the refrigeration systems of the supermarket.Malli-ennustava lähtökohta energia tarpeen hallintaan Tuiran S-Marketille. Tiivistelmä. Ekologisen jalanjäljen laskeminen on ratkaisevaa modernin elämäntavan jatkamisen ylläpitämiseksi. Hiilipohjaisista polttoaineista luopuminen sähköverkon ylläpitämiseksi on merkittävä askel kohti tätä tavoitetta. Uusiutuvista lähteistä, kuten aurinko- ja tuulivoimasta, on tulossa yhä tärkeämpiä menetelmiä hiilivapaassa energiantuotannossa. Niiden integroimisessa sähköverkkoon on kuitenkin rajoituksia johtuen niiden tyypillisistä vuodenaika- ja vuorokausirytmiriippuvaissuksista. Siten investointi hiilettömään energiantuotantoon on vasta ensimmäinen osa ratkaisua, koska niiden ajoittainen luonne johtaa pullonkaulailmiöön, joissa uusiutuvan sähköntuotantokapasiteetin kasvu ei enää lisää uusiutuvan energian sitoutumista sähköverkkoon. Täysin hiilettömän sähköverkon aikaan saamiseksi tarvitaan lisää teknisiä ratkaisuja, joiden avulla uusiutuvien energialähteiden tunkeutuminen sähköverkkoon voisi nousta jopa 100 prosenttiin. Sähköverkossa on jo 1900-luvun alusta asti käytetty ylhäältä alas -arkkitehtuuria, jossa aggregaattipuoli päättää voimalaitosten tuottaman sähkön määrän. Tässä mallissa uusiutuvan energian tuotanto on ’häiriötekijä’, johon sähköntuotantopuolen on sopeuduttava olemalla valmis laskemaan energiantuotanto voimalaitoksilla. Ongelman korjaamiseksi meidän tulee uudelleen suunnitella sähköverkkoinfrastruktuuri siten että uusiutuvan energian tuotanto ei vaaranna sähköverkon vakautta ja hallittavuutta. Helpompi tapa hallita uusiutuvan energian tuotantoa on rakentaa älykkäitä mikroverkkoja, jotka ovat pieniä autonomisia sähköverkkoja, joilla on kaksisuuntaiset virransiirtomahdollisuudet. Tämä helpottaa sähköverkon operaattoreiden päätöksentekoa. Kaikkia pääverkkoon kytkettyjä mikroverkkoja voidaan ohjeistaa siten että niiden energiantuotanto ja energian kulutus edistää pääverkon vakautta ja varmistaa, että voimalaitosten sähköntuotantotarpeisiin ei synny liian nopeita muutoksia. Mikroverkkojen käyttöönoton lisäksi on kehitettävä työkaluja, joita mikroverkot voivat käyttää tasapainottamaan sähkön kysyntää, jotta sähköverkko kykenee selviytymään kaikista olosuhteista, joita voi seurata uusiutuvan energian tuotannon ajoittaisesta luonteesta. Esimerkiksi aurinkoenergian tuotantokapasiteetin lisääminen ei toimi korvaamaan perinteistä sähköntuotantoa tietyn pisteen jälkeen, jos voimme käyttää aurinkoenergialla tuotettua sähköä vain päivällä. Ratkaisu tässä on siirtää kulutus päivältä yöaikaan. Tätä varten meidän on kehitettävä energian varastointiratkaisuja. Tässä opinnäytetyössä tutkitaan mahdollisuutta hyödyntää jäähdytystä osana sähköverkon hallintaa käyttämällä kehittyneitä prosessin ohjausmenetelmiä yhdessä Internet-of-Things-lähestymistavan kanssa. Erityisesti tutkin ajatusta käyttää jäähdytysjärjestelmiä yksisuuntaisina energianieluina, joilla voidaan ajoittaa sähkökulutus uudelleen ajanjaksoihin, jolloin uusiutuvan energian tuotanto on huipussaan. Työn kohde on Oulussa sijaitsevan S-Market Tuiran höyry-puristusjäähdytyskierto, jonka kieroaineena toimii hiilidioksidi. Jarno Johannes Tenhomaa on johdattanut matemaattisen mallin vuoden 2016 diplomityössään dynaamisen lineaarisen aikainvariantin tilamallin, joka kuvastaa kierron dynamiikkaa siten kertoen jäähdytettyjen tuotteiden termisestä inertiasta ja sitä kautta kierron sähköntarpeesta. Tässä työssä johdetaan malliennakoiva säädin (Model Predictive Controller) jäähdytysjärjestelmien dynaamista mallia hyödyntäen, jonka tehtävä on ohjata myymälän jääkaappien ja pakastimien ilman lämpötiloja, joita vuorostaan jäähdytettyjen tuotteiden lämpötilat seuraavat. MPC-säätö laskee jäähdytysjärjestelmän toimilaitteelle optimaalisen ohjauksen, jolla jäähdytysjärjestelmän lämpötila poikkeaa asetusarvosta mahdollisimman vähän valitun ennustushorisontin aikana. Lisäksi pyrin kehittämään ohjausstrategioita, jotka manipuloivat jäähdytysjärjestelmien virrankulutusta säätämällä vuorostaan malliennustavan säätimen asetusarvolämpötilaa. Näiden ohjausstrategioiden tavoite on varastoida joko halpaa ja / tai hiilineutraalia energiaa energianieluihin. Varastoitu energia voidaan myöhemmin hyödyntää aikoina, jolloin sähköverkon energia on kalliimpaa ja / tai tulee vähemmän hiilineutraaleista lähteistä. Energian Spot-markkinahintaan perustuva asetusarvon valintastrategia valitsee jääkaappien ja pakastimien asetusarvolämpötiloja Nord Pool AS:n Spot-hintatietojen mukaan. Sähköverkon uusiutuvan energian osuuteen pohjautuva asetusarvolämpötilan valintastrategia perustuu Energiateollisuus -etujärjestön julkaisemaan Suomen sähköverkon sähköntuotantotietoihin. Malliennustavan säätimen ja ohjausstrategioiden vertailuanalyysin tekemiseksi olen rakentanut reaaliaikaisen interaktiivisen simulaattorin Matlab-ohjelmaan, jolla pystyy havainnollistamaan supermarketin jäähdytysjärjestelmien toimintaa ja prosessin dynamiikkaa

Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings

Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions