Smart Metering and Pricing Policy in Smart Grids

Pricing policy is one of the tools allowing the involvement of customers in the balance between the supply and the demand in smart grids. The present chapter aims at presenting the smart metering action including the bidirectional measurement of energy for smart houses equipped with renewable energies as well as the way a smart meter communicates data at the required timing to and from the control center. A typical bill establishment explaining how the net billing is produced along with a discussion about different pricing policies that the utility may adopt to reduce the peak load demand is also presented. The work is concluded by a typical simulation of a smart city modeled in LABVIEW software.

Implementation of Medicines Pricing Policies In Sub-Saharan Africa: Systematic Review

Background: High medicine prices contribute to increasing cost of healthcare worldwide. Many patients with limited resources in sub-Saharan Africa (SSA), are confronted with out-of-pocket charges, constraining their access to medicines. Different medicine pricing policies are implemented to improve affordability and availability. However, evidence on the experiences of implementations of these policies in SSA settings appears limited. To bridge this knowledge gap, we reviewed published evidence and answered the question: what are the key determinants of implementation of medicines pricing policies in SSA countries? Methods: We identified policies, examined implementation processes, key actors involved, contextual influences on and impact of these policies. We searched five databases and grey literature; screening was done in two stages following clear inclusion criteria. A structured template guided the data extraction and data analysis followed thematic narrative synthesis. The review followed best practices and reported using PRISMA guidelines.Results: Of the 5595 studies identified, 32 met the inclusion criteria. The results showed fourteen pricing policies were implemented across SSA between 2003 and 2020. These were in four domains: targeted public subsides, regulatory frameworks and direct price control, generic medicine policies and purchasing policies. Main actors involved were government, wholesalers, manufacturers, retailers, professional bodies, community members and private and public health facilities. Key contextual barriers to implementation were: limited awareness about policies, lack of regulatory capacity, and lack of price transparency in external reference pricing process. Key facilitators were: favourable policy environment on essential medicines, strong political will, and international support. Evidence on effectiveness of these policies on reducing prices of, and improving access to, medicines were mixed. Reductions in prices were reported occasionally and implementation of medicine pricing policy sometimes led to improved availability and affordability to essential medicines.Conclusions: Implementation of medicine pricing policies in SSA shows some mixed evidence of improved availability and affordability to essential medicines. It is important to understand country-specific experiences, diversity of policy actors and contextual barriers and facilitators to policy implementation. Our study suggests three policy implications: avoiding ‘one-size-fits-all’ approach, engaging both private and public sector policy actors in policy implementation and continuously monitor implementation and effects of policies. Systematic review protocol registration: PROSPERO registration number CRD42020178166.

Power of Bonus in Pricing for Crowdsourcing

We consider a simple form of pricing for a crowdsourcing system, where pricing policy is published a priori, and workers then decide their task acceptance. Such a pricing form is widely adopted in practice for its simplicity, e.g., Amazon Mechanical Turk, although additional sophistication to pricing rule can enhance budget efficiency. With the goal of designing efficient and simple pricing rules, we study the impact of the following two design features in pricing policies: (i) personalization tailoring policy worker-by-worker and (ii) bonus payment to qualified task completion. In the Bayesian setting, where the only prior distribution of workers' profiles is available, we first study the Price of Agnosticism (PoA) that quantifies the utility gap between personalized and common pricing policies. We show that PoA is bounded within a constant factor under some mild conditions, and the impact of bonus is essential in common pricing. These analytic results imply that complex personalized pricing can be replaced by simple common pricing once it is equipped with a proper bonus payment. To provide insights on efficient common pricing, we then study the efficient mechanisms of bonus payment for several profile distribution regimes which may exist in practice. We provide primitive experiments on Amazon Mechanical Turk, which support our analytical findings.

Online Learning and Optimization of (Some) Cyclic Pricing Policies in the Presence of Patient Customers

Problem definition: We consider the problem of joint learning and optimization of cyclic pricing policies in the presence of patient customers. In our problem, some customers are patient, and they are willing to wait in the system for several periods to make a purchase until the price is lower than their valuation. The seller does not know the joint distribution of customers’ valuation and patience level a priori and can only learn this from the realized total sales in every period. Academic/practical relevance: The revenue management problem with patient customers has been studied in the literature as an optimization problem, and cyclic policy has been shown to be optimal in some cases. We contribute to the literature by studying this problem from the joint learning and optimization perspective. Indeed, to the best of our knowledge, our paper is the first work that studies online learning and optimization for multiperiod pricing with patient customers. Methodology: We introduce new dynamic programming formulations for this problem, and we develop two nontrivial upper confidence bound–based learning algorithms. Results: We analyze both decreasing cyclic policies and so-called threshold-regulated policies, which contain both the decreasing cyclic policies and the nested decreasing cyclic policies. We show that our learning algorithms for these policies converge to the optimal clairvoyant decreasing cyclic policy and threshold-regulated policy at a near-optimal rate. Managerial implications: Our proposed algorithms perform significantly better than benchmark algorithms that either ignore the patient customer characteristic or simply use the standard estimate-then-optimize framework, which does not encourage enough exploration; this highlights the importance of “smart learning” in the context of data-driven decision making. In addition, our numerical results also show that combining our algorithms with smart estimation methods, such as linear interpolation or least square estimation, can significantly improve their empirical performance; this highlights the benefit of combining smart learning with smart estimation, which further increases the practical viability of the algorithms.