Sähkövarastot osana virtuaalivoimalaitosta

Opinnäytetyö toteutettiin sähkövarastoja maahantuovalle yritykselle. Tavoitteena oli selvittää sähkövarastojen merkitystä osana virtuaalivoimalaitoksia ja sähkömarkkinoiden murrosta. Olennaisena osana tähän markkinalähtöiseen kokonaisuuteen kuului sähkömarkkinoiden toiminnan selvitys ja erityisesti kysyntäjouston osuus siinä. Nykyisen ja tulevan lainsäädännön merkitystä tarkasteltiin sähkövarastojen osalta. Työssä arvioitiin sähkövarastojen taloudellista kannattavuutta nyt ja tulevaisuudessa. Työ tehtiin tutustumalla alan tieteellisiin julkaisuihin ja asiantuntijoiden seminaarimateriaaleihin. Markkinatoimijoiden ja viranomaisten verkkojulkaisut olivat myös merkittävässä roolissa kokonaisuuden muodostamisessa. Selvitystyön perusteella olemme selkeästi menossa kohti vihreää energiantuotantoa, jossa suurteollisuuden lisäksi yhä useamman kuluttajan on mukautettava sähkönkäyttöään sään mukaan vaihtelevaan uusiutuvan energian tuotantoon. Sähköä kun pitää tuottaa ja kuluttaa joka hetki yhtä paljon. Tehopohjaiseen hinnoitteluun siirtyminen ohjaa kuluttajia leikkaamaan omia kulutushuippujaan. Joustava sähkönkäyttäjä pääsee hyötymään näistä muutoksista. Yksin tai palveluntarjoajan avustuksella sähkövaraston omistavat yritykset ja kuluttajat voivat myydä kalliilla ja ostaa tuulisena ja aurinkoisena päivä akut täyteen halpaa energiaa. Myös tehopiikkien leikkaaminen on sähkövarastojen omistajille vaivatonta. Joustamattomat sähköntuottajat ja –kuluttajat joutuvat taas maksamaan nykyistä suuremman osan sähköjärjestelmän tasapainottamisen kuluista. Virtuaalivoimalaitospilotteja on käynnissä ympäri Suomea. Sähkölaitokset kokoavat omien tuotantolaitoksiensa ja teollisten yhteistyökumppaneiden lisäksi pientuottajia ja –kuluttajia yhteen ja hyödyntävät saavutettavan kysyntäjouston sähkömarkkinoilla. Palvelun tarjoajina eli aggregaattoreina toimii myös muita yhtiöitä kuin sähkölaitoksia. Sähkövarastot ovat merkittävä osa näitä projekteja. Lakien ja määräysten muutokset 2020-luvun alkupuolella tulevat laajentamaan sähkövarastojen käyttöä samalla kuin hintakehitys on alaspäin. Tällä hetkellä luodaan valmiuksia toimia ja jaetaan markkinaosuuksia tulevaisuuden sähkömarkkinoilla.This study was made for a distributor of electrical energy storages. The aim was to provide an overview on how important electrical energy storages are and will be as a part of virtual power plants and in the energy change. Essential part of this market driven research was to find out how electricity market and demand-side management are working. The thesis was implemented by examining literature and seminar materials, as well as internet material. Based on the study, it can be said that switching to green electricity system is a reality. Big industry has been active in the electricity market and adapted its consumption. In the future this is not enough. Electricity consumers also need to adapt their consumption to weather-dependent production. Electricity must be generated and consumed in precisely the same amount at every moment. Since the cost of electricity reflects the need to adapt consumption, active and flexible consumers benefit financially, if they participate in the electricity market. As an owner of electrical energy storage you can sell electricity either direct or through service provider when the price is high, and fill your storage when the price is low. It will also mean that inflexible electrical consumers and producers will have to pay a larger share of the total costs to balance the power system. Virtual power plant pilots are ongoing all over Finland. Electrical utilities are putting together also consumers on the top of industrial partners and their own manufacturing units to be able to make benefits of demand-side management at energy market. An aggregator, as these service providers are called, can be also other companies than electrical utility. Electrical energy storages are big part of these projects. In couple of years there will be legal changes which help to use these storages. Prices are also going down. At this moment companies are working hard to find the best possible way to gain market share at the future electricity market

Expression of hematopoietic markers.

<p>Panel A displayed the representative data of Flow Cytometry with double staining of c-kit-FITC (x-axis) vs CD34, CD45, or CD133 (all-APC in y-axis) in c-kit⁺ cell population before (top row) and after (middle row) Lin-dep. Human bone marrow (bottom row) was used as positive control. Panel B, C, and D represent the Mean±SEM data of CD34, CD45, and CD133, respectively. Human bone marrow cells were used as positive control in each group. *: <i>p</i><0.05, **: <i>p</i><:0.01, ***: <i>p</i><0.001; ns: no significance. Each data was compared with the corresponding control levels (Before Lin-dep.).</p

C-kit⁺ cells after purification.

<p>Panel A showed the representative data of Flow Cytometry (upper row), where c-kit-FITC was shown on x-axis, cell Events# was on y-axis, and the percentages of c-kit⁺ cells was in red histograms. The representative images of ICC from the same patient was displayed in middle row, where c-kit⁺ cells were showed in green (FITC) and nuclei were stained in blue (DAPI) at P2-P10 after c-kit MACS but without Lin-dep. Scale bar = 20 µm. Panel B displayed the Mean±SEM data from ICC (open bar) and Flow Cytometry (filled bar). The percentages of c-kit⁺ cells by ICC and Flow showed no statistical significant difference (ns) compared to P2. n = 3 patients.</p

Fibroblast specific antibody staining.

<p>Panel A showed the representative images of fibroblast antibody (Fibroblast Surface Protein in red-APC) staining on human CSCs before (a) and after (b) c-kit MACS. The 2^nd antibody-APC (c, 2^nd AB only) and human fibroblast cell line (d) were used as positive and negative controls, respectively. Cell nuclei were stained in blue (DAPI). Scale bars = 20 µm. Panel B showed the Mean±SEM data of ICC staining from three patients (n = 3 patients). ***: <i>p</i><0.001 compared with control (Before c-kit MACS).</p

Mast cell specific antibody staining.

<p>Panel A demonstrated the representative Confocal images of mast cell specific antibody staining (Tryptase, red-APC) on c-kit⁺ CSCs (a). Human bone marrow (b) was used as positive control; the inset in b was an enlarged image of a mast positive cell. Nuclei were stained in blue (DAPI). Scale bars = 20 µm. Panel B showed the Mean±SEM of ICC data from three patients (n = 3 patients). **: <i>p</i><0.001 compared to human CSCs.</p

Early cardiac transcription factor staining.

<p>Panel A showed the representative Confocal images of GATA4 (upper row, white) and Nkx2.5 (lower row, red) antibody staining on human c-kit⁺ CSCs. Insets in b and e showed the enlarged overlap images of GATA4⁺/c-kit⁺ and Nkx2.5⁺/c-kit⁺ cells, respectively. The secondary antibody (2° AB) conjugated with FITC or APC were used as negative control. c-kit was stained in green (FITC) and nuclei were stained in blue (DAPI). Scale bars = 20 µm. Panel B and C showed the Mean±SEM of GATA4 and Nkx2.5 ICC data from three patients, respectively, where single positive (c-kit⁺) and double positive (c-kit⁺/GATA4⁺, left panel; c-kit⁺/Nkx2.5⁺, right panel) cells were counted and percentages were plotted.</p

Cardiac lineage differentiation induced by 5-azacytidine.

<p>Panel A displayed the representative RT-PCR data of cardiac lineage genes (cardiac genes: <i>GATA4</i>, <i>MEF2c</i>, <i>cTnI</i>, and <i>MLC2v</i>; endothelial genes: <i>KDR</i> and <i>vWF</i>) before (Day 0) and after differentiation (Day 3, 7, 14, 21, 28, and 35). Stem cell marker (<i>c-kit</i>) and <i>GAPDH</i> were also included as a control. Total RNA of human Jurkat cell (J), human Heart (H) and human carotid artery (A) were used as negative and positive controls, respectively. DNA molecular marker (M) was shown the first column as M (bp). Panel B showed the representative Confocal images of c-kit (green-FITC), CD31 (red-APC), α-SMA (green-FITC), α-SA (red-APC), and cTnI (red-APC) before (upper) and after (lower) differentiation. Nuclei were stained in blue (DAPI). Scale bars = 20 µm. Panel C showed the MEAN±SEM of quantitative ICC data from three patients (n = 3 patients). *: <i>p</i><0.05, **: <i>p</i><:0.01, ***: <i>p</i><0.001. Each data was compared with its control levels (before differentiation).</p

C-kit⁺ cells before purification.

<p>Panel A showed the representative images on tissue section (a) of atrial appendages or freshly isolated cells before (b) and after plating (c–f) at passage 2 (P2) to P8 from the same patient. C-kit⁺ cells were showed in green (FITC, see arrows in a and b, but all green cells in c–f) and nuclei were stained in blue (DAPI). The negative controls included secondary antibody (g. 2^ndAB) alone and human Jurkat cells (h) stained with c-kit antibody. Scale bars = 20 µm. Panel B displayed the Mean±SEM data from ICC (open bar) and Flow Cytometry (filled bar). n = 4–9 patients. *<i>p</i><0.05, ***: <i>p</i><0.01, ns: no significance, all were compared to control (Tissue).</p

Lineage positive (Lin⁺) cells.

<p>Panel A showed the representative Confocal images of Lin⁺ antibody staining (red-APC) on freshly isolated cells as well as plated cells at P0, P6, and P10 with (+) and/or without (−) c-kit MACS and/or Lin-dep (Panel A, top row-with c-kit and Lin-dep; middle row with c-kit, but without Lin-dep; and bottom row without c-kit and Lin-dep). Cell nuclei were stained in blue-DAPI, and Scale bars = 20 µm. MACS were able to be performed at P1 to P2. Human bone marrow (m to o) was used as a positive control for Lin markers (red-APC). Panel B showed the Mean±SEM data from ICC. ***: <i>p</i><0.001 (P0, P6, P10 and human bone marrow) compared with control group (Fresh isolated cells). Panel C displayed the representative data of Flow Cytometry with double staining of c-kit-FITC (x-axis) and Lin-APC (y-axis) in CSCs before (upper) and after (middle) Lin-dep. Human bone marrow was used as positive control (lower). Panel D showed the Mean±SEM) data of Flow Cytometry from three patients. ***: <i>p</i><0.001 and ns: no significance; both were compared with control level (Before Lin-dep).</p

Dot plots illustrating subject level acute (enrollment, T0) over quiescent state fold change for metabolites that demonstrated intra-subject change in thrombotic MI that differed from non-thrombotic MI and sCAD controls at q<0.05.

<p>Dot plots illustrating subject level acute (enrollment, T0) over quiescent state fold change for metabolites that demonstrated intra-subject change in thrombotic MI that differed from non-thrombotic MI and sCAD controls at q<0.05.</p