Erik Dotzauer

Simple model for prediction of loads in district-heating systems

In order to improve the operation of district-heating systems, it is necessary for the energy companies to have reliable optimization routines, both computerized and manual, implemented in their organizations. However, before a production plan for the heat-producing units can be constructed, a prediction of the heat demand first needs to be determined. The outdoor temperature, together with the social behaviour of the consumers, have the greatest influence on the demand. This is also the core of the load prediction model developed in this paper. Several methodologies have been proposed for heat-load forecasting, but due to lack in measured data and due to the uncertainties that are present in the weather forecasts, many of them will fail in practice. In such situations, a more simple model may give as good predictions as an advanced one. This is also the experience from the applications analyzed in this paper.

OPTIMAL PORTFOLIOS USING LINEAR PROGRAMMING MODELS

The classical quadratic programming (QP) formulation of the well-known portfolio selection problem has traditionally been regarded as cumbersome and time consuming. This paper formulates two additional models: (i) maximin, and (ii) minimization of mean absolute deviation. Data from 67 securities over 48 months are used to examine to what extent all three formulations provide similar portfolios. As expected, the maximin formulation yields the highest return and risk, while the QP formulation provides the lowest risk and return, which also creates the efficient frontier. The minimization of mean absolute deviation is close to the QP formulation. When the expected returns are confronted with the true ones at the end of a 6-month period, the maximin portfolios seem to be the most robust of all.

Performance evaluation of adding ethanol production into an existing combined heat and power plant

In this paper, the configuration and performance of a polygeneration system are studied by modelling the integration of a lignocellulosic wood-to-ethanol process with an existing combined heat and power (CHP) plant. Data from actual plants are applied to validate the simulation models. The integrated polygeneration system reaches a total efficiency of 50%, meeting the heating load in the district heating system. Excess heat from the ethanol production plant supplies 7.9 MW to the district heating system, accounting for 17.5% of the heat supply at full heating load. The simulation results show that the production of ethanol from woody biomass is more efficient when integrated with a CHP plant compared to a stand-alone production plant. The total biomass consumption is reduced by 13.9% while producing the same amounts of heat, electricity and ethanol fuel as in the stand-alone configurations. The results showed that another feature of the integrated polygeneration system is the longer annual operating period compared to existing cogeneration. Thus, the renewable electricity production is increased by 2.7% per year.

Experiences in mid-term planning of district heating systems

District heating systems often consist of two types of units: those that only produce heat, and those that produce both heat and power, called combined heat and power units. In order to improve the operation of such systems, detailed and reliable optimization models and methods must be available. The present paper considers mid-term planning, i.e. planning of the production of heat and power for periods of up to one month. Problem features and the questions relevant on the mid-term horizon are discussed. These include the operation of fuel storage and the influence of the national tax system. A mixed integer programming model of a set of district heating systems in Sweden is developed. The major goal is to minimize the operation cost, subject to the condition of fulfilling heat demands. The main output results are the power produced and consumed each day of the planning horizon. This is important information for the hedging activities performed in the financial power market. The model has been used regularly to support an energy company with production plans. Computational results are presented.

Annual performance analysis and comparison of pellet production integrated with an existing combined heat and power plant

Three optional pellet production processes integrated with an existing biomass-based CHP plant using different raw materials (wood chips and solid hydrolysis residues) are studied. The year is divided into 12 periods, and the integrated biorefinery systems are modeled and simulated for each period. The annual economic performance of three integrated biorefinery systems is analyzed based on the simulation results. The option of pellet production integrated with the existing CHP plant with the exhaust flue gas and superheated steam as drying mediums has the lowest specific pellet production cost of 105 €/t(pellet), the shortest payback time of less than 2 years and the greatest CO(2) reduction of the three options. An advantage in common among the three options is a dramatic increase of the total annual power production and significant CO(2) reduction in spite of a small decrease of power efficiency.

Negotiated Agreements as a vehicle for Policy Learning

The paper evaluates to which extent that different designs of Voluntary Agreements (VAs) can work as catalysts for Policy Learning (PL) and thus contribute to improved policy design and management processes. Through a literature study, it is found that VAs in the form of Negotiated Agreements (NAs) are more successful in promoting PL than other types of VAs that have less focus on the participatory aspect of the policy processes. The paper contributes to the existing VA policy literature through highlighting the predominately overseen learning values of implementing NA as well as providing policy recommendations on VA learning processes.

Use of combined physical and statistical models for online applications in the pulp and paper industry

This paper discusses the accuracy of different types of models. Statistical models are based on process data and/or observations from lab measurements. This class of models are called black box models. Physical models use physical relationships to describe a process. These are called white box models or first principle models. The third group is sometimes called grey box models, being a combination of black box and white box models. Here we discuss two examples of model types. One is a statistical model where an artificial neural network is used to predict NO x in the exhaust gases from a boiler at Mälarenergi AB in Västerås, Sweden. The second example is a grey box model of a continuous digester. The digester model includes mass balances, energy balances, chemical reactions and physical geometrical constraints to simulate the real digester. We also propose that a more sophisticated model is not required to increase the accuracy of the predicted measurements.

Automatic Meter Reading Provides Opportunities for New Prognosis and Simulation Methods

The use of top-down models, for load forecasting purposes, has been the dominating method over the last decades. However, there is now a discussion regarding the performance of the top-down models, e.g. in situations with unusual weather conditions due to the lack of historical data. This paper considers an alternative bottom-up approach with a stronger relation to the laws of physics. Electricity companies in Sweden are installing automatic meter reading systems for their customers, and using the consumption data gives new possibilities when adapting the modeling parameters in a bottom-up model for each single customer. A method for analyzing individual consumption series is suggested, where different periods in time is used to divide and identify different parts of the electricity load; base load, heat load and household loads. A review of previous work is presented, and suggestions how to link the load analysis to construction parameters for an individual building is proposed.