Site choices in recreational demand: a matter of utility maximization or regret minimization?

This paper compares the Random Regret Minimization and the Random Utility Maximization models for determining recreational choice. The Random Regret approach is based on the idea that, when choosing, individuals aim to minimize their regret – regret being defined as what one experiences when a non-chosen alternative in a choice set performs better than a chosen one in relation to one or more attributes. The Random Regret paradigm, recently developed in transport economics, presents a tractable, regret-based alternative to the dominant choice paradigm based on Random Utility. Using data from a travel cost study exploring factors that influence kayakers’ site-choice decisions in the Republic of Ireland, we estimate both the traditional Random Utility multinomial logit model (RU-MNL) and the Random Regret multinomial logit model (RR-MNL) to gain more insights into site choice decisions. We further explore whether choices are driven by a utility maximization or a regret minimization paradigm by running a binary logit model to examine the likelihood of the two decision choice paradigms using site visits and respondents characteristics as explanatory variables. In addition to being one of the first studies to apply the RR-MNL to an environmental good, this paper also represents the first application of the RR-MNL to compute the Logsum to test and strengthen conclusions on welfare impacts of potential alternative policy scenarios.

In Demand: Therapeutic Services for Children and Young People Who Have Experienced Sexual Abuse

This paper describes the key findings of an NSPCC study estimating need, in the UK, for therapeutic services for children who have experienced sexual abuse. This is based upon current estimates of the prevalence and impact of sexual abuse towards children and young people against the availability of therapeutic services in the UK. Data were collected on service location, availability, scope and coverage across England, Wales, Northern Ireland and Scotland. Researchers: (1) mapped 508 services; (2) collected data from 195 services via a structured questionnaire; (3) followed up 21 service managers and 11 service commissioners with a semi-structured interview; and (4) carried out two focus groups with young people. Data were collected on service location, availability, scope and coverage The overall level of specialist provision is low, with less than one service available per 10 000 children and young people in the UK. Calculations of need indicate that 57 156 children across the UK in the last year may have been unable to access a service. Findings from services support the view that need outstrips availability; that referral routes are limited, leaving few options for young people who have been raped or seriously sexually assaulted to directly access support; that significant waiting lists mean services must focus on reactive, rather than preventive, work; and that services are less accessible for certain groups, especially sexually abused teenagers, children with disabilities and those from Black, Asian, Minority Ethnic and Refugee backgrounds. Copyright (c) 2012 John Wiley & Sons, Ltd. Key Practitioner Messages Relevant professionals must be adequately trained to talk to children about sexual abuse and to identify those vulnerable in order to identify need. Expert specialist services are well placed to share learning on early help and identification with broader children's service providers. Active steps need to be taken by commissioners in consultation with young people, voluntary sector and adult sexual violence service providers to meet the shortfall at the level of local authorities.

A Smart Grid Information System for Demand Side Participation: Remote Control of Domestic Appliances to Balance Demand

Due to the intermittent nature of renewable generation it is desirable to consider the potential of controlling the demand-side load to smooth overall system demand. The architecture and control methodologies of such a system on a large scale would require careful consideration. Some of these considerations are discussed in this paper; such as communications infrastructure, systems architecture, control methodologies and security. A domestic fridge is used in this paper as an example of a controllable appliance. A layered approach to smart-grid is introduced and it can be observed how each smart-grid component from physical cables, to the end-devices (or smart-applications) can be mapped to these set layers. It is clear how security plays an integral part in each component of the smart-grid so this is also an integral part of each layer. The controllable fridge is described in detail and as one potential smart-grid application which maps to the layered approach. A demonstration system is presented which involves a Raspberry Pi (a low-power, low-cost device representing the appliance controller).

Hierarchical demand response using dantzig-wolfe decomposition

Demand Side Management (DSM) programmes are designed to shift electrical loads from peak times. Demand Response (DR) algorithms automate this process for controllable loads. DR can be implemented explicitly in terms of Peak to Average Ratio Reduction (PARR), in which case the maximum peak load is minimised over a prediction horizon by manipulating the amount of energy given to controllable loads at different times. A hierarchical predictive PARR algorithm is presented here based on Dantzig-Wolfe decomposition. © 2013 IEEE.

Efficient predictive demand response using laguerre functions

Predictive Demand Response (DR) algorithms allow schedulable loads in power systems to be shifted to off-peak times. However, the size of the optimisation problems associated with predictive DR can grow very large and so efficient implementations of algorithms are desirable. In this paper Laguerre functions are used to significantly reduce the size of the optimisation needed to implement predictive DR, thus significantly increasing the efficiency of the implementation. © 2013 IEEE.