تجزیه و تحلیل پاسخگویی بار صنعتی برای قیمت گذاری زمان واقعی یک بخشی Industrial Power Demand Response Analysis for One-Part Real-Time Pricing

Since the introduction of DSM in the 1970’s, load management projects mainly concentrated on residential loads. Some of the projects have resulted in a fair profitability, but many of the programs have not succeeded in achieving the established objectives, mainly due to the size of load per control point. Bjark [l] stated that it is likely that applications with low cost per controlled load may be found in industry, where the controllable load per control point is relatively large. Flory et a1 [2] reported that at many utilities 2-10% of the industrial customers account for at least 80% of the electricity usage, which emphasises the economic feasibility of DSM programs in the industrial sector. In the South Afiican situation the industrial load dominates, which motivated the local utility, ESKOM, to introduce a Key Customer focus group to promote marketing and customer services to its large industrial customers. In the view of these observations, this study focuses on demand management in the industrial sector. The formulation of utility DSM goals is largely influenced by the utility’s characteristics and external operating environment. Although utilities can offer a wide range of inducements and incentives to encourage customer participation in a particular DSM program, ultimately it is the customer decision to participate which influences the success of the activity. DSM approaches and techniques should involve a partnership between the utility and its customers, seeking common ground to maximise mutual benefit. This process will eventually lead to a customised pricing agreement between a supply utility and a customer who is willing to participate in the DSM program. Parties involved in a customised pricing process should be aware of the structures of various tariff options, and they should have knowledge of the possible impact of these DSM tariffs on the performance criteria of both the utility and the customer. Although time of use (TOU) pricing represented a significant step towards efficient electricity pricing, there is a growing recognition thal dynamic tariff forms can be more efficient. Dynamic pricing broadly encompasses tariff structures that have one or more elements which can be calculated and posted close to the time of applicability [4]. This definition embraces several concepts developed in the pricing literature, such as real-time (spot) pricing and other forms of “innovative” rates. The theory behind this pricing strategy is well documented [5]. By reflecting the “real” cost of electricity to the consumer through variable prices for specific – generally one hour – time periods, the utility provides the consumer with the information necessary to make economically sound load management decisions. Benefits of spot pricing for a customer are shown to increase with [3]: 0 These observations were made in [3] by means of a linear program (LP) based optiimisation algorithm. The purpose of this paper is to add more insight into the electricity cost saving potential of real-time pricing (RTP) through intelligent demand management. The analytical approach as illustrated, will enable utilities and industrial end users of the magnitude of price changes over time; the magnitude of the customer’s storage capacity; the amount of his peak production capacity. electricity to acquire a better knowledge of the benefits that RTP can offer. One of these benefits, i.e. the electricity cost saving potential, will be addressed in this paper. It will be mathematically presented as a function of variables that describe the structure of the real-time prices, as well as the configuration of the industrial plant, which includes the spare energy consumption capacity of the end user and the installed power consumption capacity. This approach is unique and contributes to knowledge in this field of research. A load scheduling strategy which may result in minimum electricity costs to the end user, is presented in section 11. The feasibility of the strategy depends on certain assumptions, which will be given. The mathematical modelling of the price duration curve (hourly marginal rate duration curve) is introduced in section 111. In section IV mathematical expressions of the electricity costs of an end user under onepart RTP tariff structures are derived. Section V presents the mathematical expression of the electricity cost saving potential under RTP, together with some case studies to graphically display the impacts of some important factors on the saving potential. Conclusions follow in section VI.