Tactical techno-economic analysis of electricity generation from forest, fossil, and wood waste fuels in a heating plant: Increasing Use of Renewable Forest Fuels and Local Technology Rates University of Eastern Finland Faculty of Science and Forestry Teijo Palander

June 18, 2016University of Eastern Finland2 STRATEGIC LEVEL PLANNING PROCEDURE ENERGY PRODUCTION OF PLANT SUPPLY FROM PRIVATE FORESTS ENERGY-FUEL DEMAND EXCHANGES IMPORTS PURCHASES FROM OTHER SUPPLIERS ADJUSTMENT OF ENERGY RESOURCES AND DEMAND CHANGES IN ENERGY- FUEL MIXTURES CHANGES IN ENERGY- FUEL INVENTORIES COMPANY FORESTS PROCUREMENT PLAN CHANGES IN ENERGY- PRODUCT MIXTURES CHANGES IN ENERGY- PRODUCT INVENTORIES Tactical planning operates in this Box

June 18, 2016University of Eastern Finland3 Dynamics of energy-resource inventories for an energy (CHP) plant … system description for energy-flow model Renewable forest fuelsOther fuels and electricity Horizontal arrows describe time-dependent effects of system. Vertical arrows describe sequence-dependent effects of system. Energy-fuel procurement and electricity production schedules, MWh/Month, were optimized for a year using dynamic (D)MOLP-model and Simplex method. ADJUSTMENT of DEMAND & SUPPLY & PROCUREMENT

June 18, 2016University of Eastern Finland4 INTERACTIVE RESULTS DISPLAY Decision-making process is supported in MS Windows XP Professional operating system, e.g., results are visualized with a user interface.

June 18, 2016University of Eastern Finland5 TACTICAL ENERGY-FUEL PROCUREMENT PROBLEM Research area locates in the southern Finland, which is described by the municipalities on the map. Definition of test — Local harvesting conditions affect energy-fuel procurement during procurement year. — What will be electricity production and fuel procurement schedules, if one harvesting team can not deliver energy wood to plant according to strategic plan during three months??? — Other teams can increase their wood procurement responsibilities and deliver more energy wood to plant. A test series were run based on real-life data from the energy-production industry.

June 18, 2016University of Eastern Finland6 ILLUSTRATIVE EXAMPLES OF THE TEST Examples A1,A2,A3,B1,B2, B3: The energy-fuel procurement was optimized using a strategic planning model, i.e., the model was formulated in accordance with the energy production and energy-fuel procurement without a tactical adjustment approach. Teams’ local forest technology rates were at the same level. ( * Examples B2T1, B2T2: As the example B2 (forest technology rates <300% over real-life) was formulated in accordance with the energy-fuel procurement using a tactical adjustment approach. Local forest technology rates were <150% (Team C, Jaala) and <450% (Teams A,B, and D). Using operation analyses we could discuss the results for the impacts of local forest technology rates on energy production and energy-fuels procurement, because the example B2S (C<150%; A,B,D<300 %) has no feasible solution, when the production and procurement schedules of B2 were used. Furthermore, we could draw conclusion for the efficiency of DSS. Palander T Technical and economic analysis of electricity generation from forest, fossil, and wood-waste fuels in a Finnish heating plant. Energy 36, (*

June 18, 2016University of Eastern Finland7 STRATEGIC AND TACTICAL DECISION ALTERNATIVES Strategic Scenario Total costsTotal revenuesTotal loss costs I, €/1000 Goal, Million € D, €/1000 I, €/1000 Goal, Million € D, €/1000 I, €/1000 Goal, Million € D, €/1000 A A A B B B Tactical example B2T B2T Ranges of objective values for globally optimal non-dominated decision alternatives for supply chain management of a CHP plant. I = Increase, D = Decrease A1, A2, and A3 = supply chains that include an energy efficiency of 19% and forest technology rates of 100, 300, and 1000%, respectively. B1, B2, and B3 = supply chains that include an energy efficiency of 42% and forest technology rates of 100, 300, and 1000%, respectively. B2T1 and B2T2 = supply chains that include an energy efficiency of 42% and local forest technology rates of <150% (Team C, Jaala) and <450% (other teams).

June 18, 2016University of Eastern Finland8 STRATEGIC PROCUREMENT SCHEDULE Example B2: Energy fuel procurement mixture (as produced energy) without adjusted forest technology rates, MWh/Month. Relative level of below-ground forest wood procurement was low compared to levels of the other ”green fuels” and nonrenewable energy fuels. Tactical planning period

June 18, 2016University of Eastern Finland9 STRATEGIC PROCUREMENT PLAN Annual fuel mixtures of the peat and forest fuels. A1, A2, and A3, supply chains that include an energy efficiency of 19% and forest technology rates of 100, 300, and 1000%, respectively. B1, B2, and B3, supply chains that include an energy efficiency of 42% and forest technology rates of 100, 300, and 1000%, respectively.

June 18, 2016University of Eastern Finland10 STRATEGIC PRODUCTION SCHEDULE Electricity production A1, A2, and A3, supply chains that include an energy efficiency of 19% and forest technology rates of 100, 300, and 1000%, respectively. B1, B2, and B3, supply chains that include an energy efficiency of 42% and forest technology rates of 100, 300, and 1000%, respectively.

June 18, 2016University of Eastern Finland11 STRATEGIC ELECTRICITY SALES Electricity sales in the supply chains for scenarios A1, A2, and A3, which include energy efficiencies of 19% and forest technology rates of 100, 300, and 1000%, respectively.

June 18, 2016University of Eastern Finland12 STRATEGIC ELECTRICITY SALES Electricity sales in the supply chains for scenarios B1, B2, and B3, which include energy efficiencies of 42% and forest technology rates of 100, 300, and 1000%, respectively.

June 18, 2016University of Eastern Finland13 TACTICAL PROCUREMENT SCHEDULE Procurement schedules of a forest energy-fuel (below-ground) for teams (A,B,C,D) in the example (B2T2) that includes a tactical adaptation of strategic plan for three months. Energy efficiency of 42% Forest technology rates of <150% (Team C) and <450% (Teams A,B, and D).

8 th period Wood waste fuels, MWh Fossil & peat fuels, MWh Forest fuels, MWh Example Liquids Mill wood, own Mill wood, market OilCoalPeat fuelForest chips Forest Wood, overground Forest wood, underground B B2T June 18, 2016University of Eastern Finland14 ENERGY-FUEL MIXTURES OF AUGUST The energy-fuel mixtures: B2 = Model without local forest technology rates, B2T2 = Model with local forest technology rates. The changes in the forest energy-fuel volume (MWh) were achieved by the use of local forest technology rate and minor annual increase of peat fuel (1.3%).

June 18, 2016University of Eastern Finland15 CONCLUSIONS — According to the results, the strategic procurement plan can be adjusted to changed decision-making environment using tactical planning process. — Tactical decision support methodology generated global optimal solution within a reasonable computational time. — The methodology can be used as a powerful core of a decision support system and has great potential for the significant improvement of information logistics in energy production and energy-fuel procurement. PRACTICAL ADVANTAGE This approach saves costs, because tactical planning process is fast and cheap and the expensive strategic planning process is not necessary to start from the beginning.

June 18, 2016University of Eastern Finland16 PERFORMANCE OF DECISION SUPPORT SYSTEM Solution performance: B2 = without local forest technology rates, B2S = with local forest technology rate for team C, B2T2 = with local forest technology rates for teams ABCD. ExampleSolution optimality CPU- seconds LP- iterations B2Global49358 B2SNo feasible solution B2T2Global54504 WE RECOMMEND THIS KIND EFFICIENT METHODOLOGY TO YOU Thank you!