Managing Permit Markets to Stabilize Prices

The political economy of environmental policy favors the use of quantity-based instruments over price-based instruments (e.g., tradable permits over green taxes), at least in the United States. With cost uncertainty, however, there are clear efficiency advantages to prices in many cases, especially for stock pollutants such as greenhouse gases. The question arises, therefore, of whether one can design flexible quantity policies that mimic the behavior of price policies, namely stable permit prices and abatement costs. We explore a number of “quantity-plus” policies that replicate the behavior of a price policy through rules that adjust the effective permit cap for unexpectedly low or high costs. They do so without necessitating any monetary exchanges between the government and the regulated firms, which can be a significant political barrier to the use of price instruments.permit market, prices, quantities, banking, borrowing, uncertainty

Controlling a stock pollutant with endogenous investment and asymmetric information

Non-strategic firms with rational expectations make investment and emissions decisions. The investment rule depends on firms' beliefs about future emissions policies. We compare emissions taxes and quotas when the (strategic) regulator and (nonstrategic) firms have asymmetric information about abatement costs, and all agents use Markov Perfect decision rules. Emissions taxes create a secondary distortion at the investment stage, unless a particular condition holds; emissions quotas do not create a secondary distortion. We solve a linear-quadratic model calibrated to represent the problem of controlling green-house gases. The endogeneity of investment favors taxes, and it increases abatement

Operation efficiency optimisation modelling and application of model predictive control

The efficiency of any energy system can be characterised by the relevant efficiency components in terms of performance, operation, equipment and technology (POET). The overall energy efficiency of the system can be optimised by studying the POET energy efficiency components. For an existing energy system, the improvement of operation efficiency will usually be a quick win for energy efficiency. Therefore, operation efficiency improvement will be the main purpose of this paper. General procedures to establish operation efficiency optimisation models are presented. Model predictive control, a popular technique in modern control theory, is applied to solve the obtained energy models. From the case studies in water pumping systems, model predictive control will have a prosperous application in more energy efficiency problems

Medium density control for coal washing dense medium cyclone circuits

The dense medium cyclone (DMC) process used in coal beneficiation plants is studied from a control system perspective. Employing the dynamic model of the DMC process derived from mass flow balance, a model-based control strategy is proposed. The controller adjusts the density of medium used to enhance separation in the DMC process according to measurements on percentages of different components in raw coal. The first objective of the control is to maintain the carbon content in the clean coal to a set level. The second purpose is to minimize energy consumption of the DMC process in view of the fast increasing electricity price. The controller solves an optimization problem formulated to determine the density of medium whenever new measurements are available. Both coal quality and DMC operational constraints are accounted for. Simulations, based on measured plant data, are carried out to verify the feasibility and effectiveness of the control strategy designed. The results show that the designed controller is able to fulfill its purpose satisfactorily when the characteristics of the raw coal varies and when measurement uncertainties are in presence

Optimal sampling plan for clean development mechanism lighting projects with lamp population decay

This paper proposes a metering cost minimisation model that minimises metering cost under the constraints of sampling accuracy requirement for clean development mechanism (CDM) energy efficiency (EE) lighting project. Usually small scale (SSC) CDM EE lighting projects expect a crediting period of 10 years given that the lighting population will decay as time goes by. The SSC CDM sampling guideline requires that the monitored key parameters for the carbon emission reduction quantification must satisfy the sampling accuracy of 90% confidence and 10% precision, known as the 90/10 criterion. For the existing registered CDM lighting projects, sample sizes are either decided by professional judgment or by rule-of-thumb without considering any optimisation. Lighting samples are randomly selected and their energy consumptions are monitored continuously by power meters. In this study, the sampling size determination problem is formulated as a metering cost minimisation model by incorporating a linear lighting decay model as given by the CDM guideline AMS-II.J. The 90/10 criterion is formulated as constraints to the metering cost minimisation problem. Optimal solutions to the problem minimise the metering cost whilst satisfying the 90/10 criterion for each reporting period. The proposed metering cost minimisation model is applicable to other CDM lighting projects with different population decay characteristics as well

Coordinated two-stage volt/var management in distribution networks

This paper investigates daily volt/var control in distribution networks using feeder capacitors as well as substation capacitors paired with on-load tap changers. A twostage coordinated approach is proposed. Firstly, the feeder capacitor dispatch schedule is determined based on reactive power heuristics. Then, an optimisation model is applied to determine the dispatch schedule of the substation devices taking into account the control actions of the feeder capacitors. The reference voltage of the substation secondary bus and the tap position limits of transformers are modified such that the model adapts to varying load conditions. The optimisation model is solved with a modified particle swarm optimisation algorithm. Furthermore, the proposed method is compared with conventional volt/var control strategies using a distribution network case study. It is demonstrated that the proposed approach performs better than the conventional strategies in terms of voltage deviation and energy loss minimisation