Short-term Self-Scheduling of Virtual Energy Hub Plant within Thermal Energy Market

Multicarrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among multiple energy systems and energy hubs in different energy markets. By the advent of the local thermal energy market in many countries, energy hubs' scheduling becomes more prominent. In this article, a new approach to energy hubs' scheduling is offered, called virtual energy hub (VEH). The proposed concept of the energy hub, which is named as the VEH in this article, is referred to as an architecture based on the energy hub concept beside the proposed self-scheduling approach. The VEH is operated based on the different energy carriers and facilities as well as maximizes its revenue by participating in the various local energy markets. The proposed VEH optimizes its revenue from participating in the electrical and thermal energy markets and by examining both local markets. Participation of a player in the energy markets by using the integrated point of view can be reached to a higher benefit and optimal operation of the facilities in comparison with independent energy systems. In a competitive energy market, a VEH optimizes its self-scheduling problem in order to maximize its benefit considering uncertainties related to renewable resources. To handle the problem under uncertainty, a nonprobabilistic information gap method is implemented in this study. The proposed model enables the VEH to pursue two different strategies concerning uncertainties, namely risk-averse strategy and risk-seeker strategy. For effective participation of the renewable-based VEH plant in the local energy market, a compressed air energy storage unit is used as a solution for the volatility of the wind power generation. Finally, the proposed model is applied to a test case, and the numerical results validate the proposed approach

Policy Evaluation in Uncertain Economic Environments

This paper develops a general framework for economic policy evaluation. Using ideas from statistical decision theory, it argues that conventional approaches fail to appropriately integrate econometric analysis into evaluation problems. Further, it is argued that evaluation of alternative policies should explicitly account for uncertainty about the appropriate model of the economy. The paper shows how to develop an explicitly decision-theoretic approach to policy evaluation and how to incorporate model uncertainty into such an analysis. The theoretical implications of model uncertainty are explored in a set of examples, with a specific focus on how to design policies that are robust against such uncertainty. Finally, the framework is applied to the evaluation of monetary policy rules and to the analysis of tariff reductions as a way to increase aggregate economic growth.macroeconomics, Policy Evaluation, Uncertain Economic Environments

Risk Management in Action. Robust monetary policy rules under structured uncertainty.

Recent interest in ‘Risk Management’ has highlighted the relevance of Bayesian analysis for robust monetary- policy making. This paper sets out a comprehensive methodology for designing policy rules inspired by such considerations. We design rules that are robust with respect to model uncertainty facing both the policy-maker and private sector. We apply our methodology to three simple interest-rate rules: inflation-forecast- based (IFB) rules with a discrete forward horizon, one targeting a discounted sum of forward inflation, and a current wage inflation rule. We use an estimated DSGE model of the euro area and estimated measures of structured exogenous and parameter uncertainty for the exercise. We find that IFB rules with a long horizon perform poorly with or without robust design. Our discounted future targeting rule performs much better, indicating that policy can be highly forward-looking without compromising stabilization. The wage inflation rule dominates whether it is designed to have good robust properties or not. JEL Classification: E52, E37, E58Interest-rate rules, Robustness, structured uncertainty

Monetary policy analysis with potentially misspecified models

The paper proposes a novel method for conducting policy analysis with potentially misspecified dynamic stochastic general equilibrium (DSGE) models and applies it to a New Keynesian DSGE model along the lines of Christiano, Eichenbaum, and Evans (JPE2005) and Smets and Wouters (JEEA2003). We first quantify the degree of model misspecification and then illustrate its implications for the performance of different interest-rate feedback rules. We find that many of the prescriptions derived from the DSGE model are robust to model misspecification.Monetary policy