The branching ratio of the two modes of decay of Fm'", i.e. , E.C. /n, was found to be about 8.5which gives 89.5% decay by electron capture and 10.5% by alpha emission. It was not possible to measure the cross section for the Cf'"(n, 3n)Fm'" reaction because Fm'" could also be produced from other californium isotopes in the target. A previous publication4 on a possible identification of the Fm'" gave the values of 6.85&0.04 Mev for the alpha-particle energy, and a half-life &10 days. It is a pleasure to thank the crew of the 60-inch cyclotron for their extremely careful and skillful operation of the machine during the bombardment. We wish to thank Professor Glenn T. Seaborg for his continued interest.

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Distributed energy resources and demand side management are expected to become more prevalent in the future electric power system. Coordinating the increased number of grid participants in an efficient and reliable way is going to be a major challenge. A potential solution is the employment of a distributed energy management approach which uses intelligence distributed over the grid to balance supply and demand. In this paper, we specifically consider the situation in which distributed resources and loads form microgrids within the bulk power system in which load is supplied by local generation. A distributed energy management approach based on the consensus + innovations method is presented and used to coordinate local generation, flexible load and storage devices within the microgrid. The approach takes advantage of the fact that in the optimum the marginal costs given as a function of the power output/consumption needs to be equal for all the network entities (agents). Solutions for single time step as well as multi time step optimization including inter-temporal constraints are presented.

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The sharing economy has upset market for housing and transportation services. Homeowners can rent out their property when they are away on vacation, car owners can offer ridesharing services. These sharing economy business models are based on monetizing under-utilized infrastructure. They are enabled by peer-to-peer platforms that match eager sellers with willing buyers.Are there compelling sharing economy opportunities in the electricity sector? What products or services can be shared in tomorrow's Smart Grid? We begin by exploring sharing economy opportunities in the electricity sector, and discuss regulatory and technical obstacles to these opportunities. We then study the specific problem of a collection of firms sharing their electricity storage. We characterize equilibrium prices for shared storage in a spot market. We formulate storage investment decisions of the firms as a non-convex non-cooperative game. We show that under a mild alignment condition, a Nash equilibrium exists, it is unique, and it supports the social welfare. We discuss technology platforms necessary for the physical exchange of power, and market platforms necessary to trade electricity storage. We close with synthetic examples to illustrate our ideas.

We propose a novel demand side management method to tackle the intermittency in wind power generation. Our focus is on an isolated microgrid with one wind turbine, one fastresponding conventional generator, and several users. Users act as independent decision makers in shaping their own load profiles. Using dynamic potential game theory, we analyze and coordinate the interactions among users to efficiently utilize the available renewable and conventional energy resources to minimize the total energy cost in the system. We further model the intertemporal variations of the available wind power as a Markov chain based on field data. Using techniques from dynamic potential game theory, we first derive closed-form expressions for the best responses for the users that participate in demand side management. Then, we investigate the efficiency of the constructed game model at the equilibrium. Finally, the system performance is assessed using computer simulation. In particular, our proposed scheme saves 38% generation cost compared with the case without demand side management.