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Energy efficiency is the most readily available, rapid and cost-effective way to achieve desired greenhouse gases reductions. Therefore, it is the focus of energy and climate change policies world wide. The results of these policies are still missing in the desired extent, even in the EU, which has the most advanced energy efficiency policy. The main reason behind this policy failure is a complete lack of focus on implementing capacities that would ensure full policy uptake. Embracing full-scale energy management systems in public and business sectors and mobilisation of and cooperation between all stakeholders are the way towards higher efficiency.

This paper provides insights into performance of competing agents in Power TAC trial held in March 2013. Firstly, the paper gives the description of the Power TAC post-game data set and presents our analysis process. Furthermore, paper discusses the analysis output: indicators about brokers’ performance in energy retail market, energy wholesale market as well as the balancing process. Results of the analysis identified diverse approaches in the design of competing agents’ strategies, suggesting there will be interesting and successful Power TAC 2013 finals.

We present a total of 191 and 203 data points of the elastic neutron-protonspin correlation parameters Aookk and Aoosk , respectively. Both observables were measured in a large angular interval. The observable Aookk was measured from 0.312 to 1.10 GeV and Aoosk from 0.80 to 1.10 GeV. The SATURNE II polarized beam of free neutrons obtained from the break-up of polarized deuterons was scattered on the polarized Saclay frozen-spin proton target. The beam polarization was oriented either along the beam direction or sideways, the target polarization was oriented longitudinally. Data are compared with phase-shift analyses predictions and with the PSI, LAMPF and SATURNE II results. Present results provide an important contribution to any future theoretical or phenomenological analysis.

We model the retail market with dynamic pricing as a Stackelberg game where both retailers (leaders) and flexible consumers (followers) solve an economic cost-minimization problem. The electricity retailer optimizes an economic objective over a daily horizon by setting an hourly price-sequence, which is then communicated to the end-consumers. In turn, on the basis of such price sequence, consumers optimize a utility function that accounts both for energy procurement costs and for the benefit loss resulting from deferring consumption. The game is formulated as a Mathematical Problem with Equilibrium Constraints (MPEC) and cast as a Mixed Integer Linear Program (MILP), which can be solved using off-the-shelf optimization software. In an illustrative example, we consider a retailer associated with both flexible demand and wind power production. Such an example shows the efficiency of dynamic pricing as a way to control the load for minimizing the imbalances due to wind power, assesses the overall economic results for the retailer and the consumers as well as the dynamic properties of consumer flexibility.