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Efficiency of energy resource use is a key factor for a sustainable energy future. By matching the exergy levels of supply and demand, Energy Quality Management (EQM) of building energy supply systems may achieve more efficient use of energy resources. Beyond this, environmental impact of energy supply systems is another essential issue. This work addresses the influence of EQM on CO 2 emissions in the operation optimization of a Distributed Energy System (DES). A multi-objective linear programming problem is formulated to reduce energy costs and increase the overall exergy efficiency. Total CO 2 emissions are evaluated for the optimized operation strategies of the DES. The operators of the DES can choose the operation strategy from the Pareto front, based on their priorities and also aware of effects on CO 2 emissions. Results demonstrate more efficient use of energy resources and reduction in CO 2 emissions through EQM, as compared with conventional energy supply systems.

Efficiency of energy resource use is a key factor for a sustainable energy future. By matching the exergy levels of supply and demand, Energy Quality Management (EQM) of building energy supply systems may achieve more efficient use of energy resources. Beyond this, environmental impact of energy supply systems is another essential issue. This work addresses the influence of EQM on CO 2 emissions in the operation optimization of a Distributed Energy System (DES). A multi-objective linear programming problem is formulated to reduce energy costs and increase the overall exergy efficiency. Total CO 2 emissions are evaluated for the optimized operation strategies of the DES. The operators of the DES can choose the operation strategy from the Pareto front, based on their priorities and also aware of effects on CO 2 emissions. Results demonstrate more efficient use of energy resources and reduction in CO 2 emissions through EQM, as compared with conventional energy supply systems.

Cogeneration of heat and electricity is an important pillar of energy and climate policy. To plan the production and distribution system of combined heat and power (CHP) systems for residential heating, suitable methods for decision support are needed. For a comprehensive feasibility analysis, the integration of the location and capacity planning of the power plants, the choice of customers, and the network planning of the heating network into one optimization model are necessary. Thus, we develop an optimization model for electricity generation and heat supply. This mixed integer linear program (MILP) is based on graph theory for network flow problems. We apply the network location model for the optimization of district heating systems in the City of Osorno in Chile, which exhibits the “checkerboard layout” typically found in many South American cities. The network location model can support the strategic planning of investments in renewable energy projects because it permits the analysis of changing energy prices, calculation of break-even prices for heat and electricity, and estimation of greenhouse gas emission savings.

Cogeneration of heat and electricity is an important pillar of energy and climate policy. To plan the production and distribution system of combined heat and power (CHP) systems for residential heating, suitable methods for decision support are needed. For a comprehensive feasibility analysis, the integration of the location and capacity planning of the power plants, the choice of customers, and the network planning of the heating network into one optimization model are necessary. Thus, we develop an optimization model for electricity generation and heat supply. This mixed integer linear program (MILP) is based on graph theory for network flow problems. We apply the network location model for the optimization of district heating systems in the City of Osorno in Chile, which exhibits the “checkerboard layout” typically found in many South American cities. The network location model can support the strategic planning of investments in renewable energy projects because it permits the analysis of changing energy prices, calculation of break-even prices for heat and electricity, and estimation of greenhouse gas emission savings.

This paper presents an optimal control strategy for a district heating power plant with thermal energy storage. The main goal of the control strategy is to reduce the operation costs of the power plant, by scheduling the boilers, the operation of the thermal energy storage and the curtailment on the loads. The problem is stated as a constrained optimization in the form of a Mixed Integer Linear Program (MILP), embedded on an Model Predictive Control (MPC) framework. Particular attention is paid to modeling of boilers operating constraints, including the outlet water flow temperature, to the energy exchanged with the thermal energy storage and to the operating modes of the power plant layout, including the constraints related to the supply water temperature needed from the network. The results are performed using the data and the layout of the power plant located in the city of Ylivieska, in Finland. The cost analysis performed shows the advantages of using the predictive control strategy.

This paper presents an optimal control strategy for a district heating power plant with thermal energy storage. The main goal of the control strategy is to reduce the operation costs of the power plant, by scheduling the boilers, the operation of the thermal energy storage and the curtailment on the loads. The problem is stated as a constrained optimization in the form of a Mixed Integer Linear Program (MILP), embedded on an Model Predictive Control (MPC) framework. Particular attention is paid to modeling of boilers operating constraints, including the outlet water flow temperature, to the energy exchanged with the thermal energy storage and to the operating modes of the power plant layout, including the constraints related to the supply water temperature needed from the network. The results are performed using the data and the layout of the power plant located in the city of Ylivieska, in Finland. The cost analysis performed shows the advantages of using the predictive control strategy.

Historical time trends indicate that both carbon and energy intensity have declined in the United States over the last several decades, while economic performance, as measured by per capita GSP, has improved. This observation indicates that it may be possible to reduce carbon intensity without a reduction in economic performance. This paper assesses using panel analysis, the empirical relationship between carbon emissions intensity and economic performance, and examines the direction of causality between the two variables. Data for the analysis covered 48 states, excluding Hawaii, Alaska, and Washington DC, from 1980 to 2000. The results indicate significant bi-directional relationship between carbon emissions intensity and state economic performance, both using an aggregate indicator for carbon emissions intensity, decomposed using Laspeyres indexes and disaggregated by sector. This implies that it should be possible to implement statewide and sector-specific policies to reduce energy and carbon intensity and at the same time improve economic performance.

Historical time trends indicate that both carbon and energy intensity have declined in the United States over the last several decades, while economic performance, as measured by per capita GSP, has improved. This observation indicates that it may be possible to reduce carbon intensity without a reduction in economic performance. This paper assesses using panel analysis, the empirical relationship between carbon emissions intensity and economic performance, and examines the direction of causality between the two variables. Data for the analysis covered 48 states, excluding Hawaii, Alaska, and Washington DC, from 1980 to 2000. The results indicate significant bi-directional relationship between carbon emissions intensity and state economic performance, both using an aggregate indicator for carbon emissions intensity, decomposed using Laspeyres indexes and disaggregated by sector. This implies that it should be possible to implement statewide and sector-specific policies to reduce energy and carbon intensity and at the same time improve economic performance.

In order to protect our health and the environment, numerous limit and guideline values have been laid down for substances and contaminants that need to be regularly monitored. In such cases, the result of an analytical measurement is compared with its corresponding limit value, and a conclusion is then drawn as to whether the given limit has been violated or not. A simple approach to this would be to simply check whether the measured value is higher than the limit value or not. However, this approach is only acceptable when the measured value is far from the limit value. In cases where the analytical result is close to the limit value, the measurement uncertainty must also be taken into account when deciding whether an observed limit value transgression is significant or not—as, for example, demanded in DIN EN 17025 [1]. Therefore, it is very important that chemical laboratories that perform routine tests for limit value transgression determine the measurement uncertainties associated with their methods by long-term validation [2, 3]. However, even in cases where a measurement result is presented together with its measurement uncertainty, clear rules should be established for comparing the measured value with a limit or guideline value [4, 5]. If such rules are misunderstood or ambiguous, feelings of insecurity can arise among the decision makers. If systematic errors are excluded, the measurement uncertainty can be reduced to random errors and dealt with statistically. Therefore, statistical concepts can be used to unobjectionably and reproducibly justify a decision made about whether a limit value has been violated or observed, and on the other hand to estimate the analytical effort required to achieve significant results. A test result leading to the statement that the limit value μ0 (in analogy to the terminology of clinical chemistry [7, 8]) has been transgressed will be called “positive,” while an inconspicious result will be termed “negative.” In this case, a test for a limit value transgression can lead to four possible outcomes, as listed in Table 1. Two types of erroneous outcome can occur, namely a false-positive (type I error) or a false-negative (type II error) result. The probability of a type I error is usually denoted α, and that of a type II error β.